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Journal articles on the topic 'Attribute Similarity'
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Wan, Yongquan, Cairong Yan, Guobing Zou, and Bofeng Zhang. "Attribute-guided and attribute-manipulated similarity learning network for fashion image retrieval." Intelligent Data Analysis 27, no. 3 (2023): 733–51. http://dx.doi.org/10.3233/ida-226740.
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Learning the similarity between fashion items is essential for many fashion-related tasks. Most methods based on global or local image similarity cannot meet the fine-grained retrieval requirements related to attributes. We are the first to clearly distinguish the concepts of attribute name and their values and divide fashion retrieval tasks that combine images and text into: attribute-guided retrieval and attribute-manipulated retrieval. We propose a hierarchical attribute-aware embedding network (HAEN) that takes images and attributes as input, learns multiple attribute-specific embedding spaces, and measures fine-grained similarity in the corresponding spaces. It can accurately map different attributes to the corresponding areas of the image, thereby facilitating the feature fusion of two different modalities of text and image, including enhancement and replacement. Then on this basis, we propose three attribute-manipulated similarity learning methods, HAEN_Avg, HAEN_Rec, and HAEN_Cmb. With comprehensive validation on two real-world fashion datasets, we demonstrate that our methods can effectively leverage semantic knowledge to improve image retrieval performance, including attribute-guided and attribute-manipulated retrieval tasks.
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Lin, Mugang, Kunhui Wen, Xuanying Zhu, Huihuang Zhao, and Xianfang Sun. "Graph Autoencoder with Preserving Node Attribute Similarity." Entropy 25, no. 4 (2023): 567. http://dx.doi.org/10.3390/e25040567.
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The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while ignoring the preservation of the attribute information of nodes. Thus, the node attributes cannot be fully learned and the ability of the GAE to learn higher-quality representations is weakened. To address the issue, this paper proposes a novel GAE model that preserves node attribute similarity. The structural graph and the attribute neighbor graph, which is constructed based on the attribute similarity between nodes, are integrated as the encoder input using an effective fusion strategy. In the encoder, the attributes of the nodes can be aggregated both in their structural neighborhood and by their attribute similarity in their attribute neighborhood. This allows performing the fusion of the structural and node attribute information in the node representation by sharing the same encoder. In the decoder module, the adjacency matrix and the attribute similarity matrix of the nodes are reconstructed using dual decoders. The cross-entropy loss of the reconstructed adjacency matrix and the mean-squared error loss of the reconstructed node attribute similarity matrix are used to update the model parameters and ensure that the node representation preserves the original structural and node attribute similarity information. Extensive experiments on three citation networks show that the proposed method outperforms state-of-the-art algorithms in link prediction and node clustering tasks.
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Alharthi, T. N., M. A. Elsafty, and Lishan Liu. "Attribute topologies based similarity." Cogent Mathematics 3, no. 1 (2016): 1242291. http://dx.doi.org/10.1080/23311835.2016.1242291.
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Yager, Ronald, Fred Petry, and Paul Elmore. "Multiple attribute similarity hypermatching." Soft Computing 22, no. 8 (2017): 2463–69. http://dx.doi.org/10.1007/s00500-017-2721-5.
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Aynulin, Rinat, and Pavel Chebotarev. "Extending Proximity Measures to Attributed Networks for Community Detection." Complex Systems 30, no. 4 (2021): 441–55. http://dx.doi.org/10.25088/complexsystems.30.4.441.
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Proximity measures on graphs are extensively used for solving various problems in network analysis, including community detection. Previous studies have considered proximity measures mainly for networks without attributes. However, attribute information, node attributes in particular, allows a more in-depth exploration of the network structure. This paper extends the definition of a number of proximity measures to the case of attributed networks. To take node attributes into account, attribute similarity is embedded into the adjacency matrix. Obtained attribute-aware proximity measures are numerically studied in the context of community detection in real-world networks.
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Liu, Xiao Jing, Wei Feng Du, and Xiao Min. "Fuzzy Attribute Reduction Based on Fuzzy Similarity." Applied Mechanics and Materials 533 (February 2014): 237–41. http://dx.doi.org/10.4028/www.scientific.net/amm.533.237.
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The measure of the significance of the attribute and attribute reduction is one of the core content of rough set theory. The classical rough set model based on equivalence relation, suitable for dealing with discrete-valued attributes. Fuzzy-rough set theory, integrating fuzzy set and rough set theory together, extending equivalence relation to fuzzy relation, can deal with fuzzy-valued attributes. By analyzing three problems of FRAR which is a fuzzy decision table attribute reduction algorithm having extensive use, this paper proposes a new reduction algorithm which has better overcome the problem, can handle larger fuzzy decision table. Experimental results show that our reduction algorithm is much quicker than the FRAR algorithm.
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Zheng, Li Ping, Guang Yao Li, Hua Jiang, and Jun Qing Li. "The Research of Ontology Mapping Based on PSO Algorithm." Key Engineering Materials 460-461 (January 2011): 172–77. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.172.
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The key of ontology mapping is to compute concepts similarities. In order to decrease errors, the computation of similarity should consider the influences of relations and attributes. In this paper, a computation method of similarity based on PSO is put forward. At first, the semantic similarity of concepts is computed. Then compute the relation similarity and the attribute similarity. In order to decrease the computation quantity, the attribute priority is specified by PSO algorithm. At last, the attribute with high priority is chosen according to the user need. Take two ontologies as example and specify the attribute priority. Experiments results show that this calculative method can improve the precision of results and reduce the calculated quantities.
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Dimitriu, Radu, Luk Warlop, and Bendik Meling Samuelsen. "Brand extension similarity can backfire when you look for something specific." European Journal of Marketing 51, no. 5/6 (2017): 850–68. http://dx.doi.org/10.1108/ejm-09-2015-0662.
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Purpose The purpose of this paper is to show that high similarity between a parent brand and an extension category can have a detrimental effect on how a brand extension is perceived to perform on specific attributes. This happens because similarity influences the perceived positioning of a brand extension: lower similarity extensions can be perceived as “specialized” products, whereas high similarity extensions are perceived as “all-in-one” products not performing exceptionally well on any specific attribute. Design/methodology/approach The authors test the hypothesized effect through three experimental studies. The authors manipulate similarity both within subjects (Study 1a) and between subjects (Study 1b and Study 2). Further, the authors test the effect for specific attributes that are physical/concrete in nature (Study 1a and Study 1b) as well as attributes that are abstract/imagery-related in nature (Study 2). Findings High compared to low similarity improves perceptions of overall performance (i.e. performance across all attributes). But as expected, the authors also find that a high similarity brand extension is perceived to perform worse on the attribute on which a low similarity brand extension specializes, even when the parent brands of the extensions possess that attribute to the same extent. This perception of attribute performance carries on to influence brand extension purchase likelihood. Practical implications The degree of brand extension similarity has consequences for how brand extensions are perceived to be positioned in the marketplace. Although high similarity extensions receive positive evaluations, they might not be suitable when a company is trying to instil a perception of exceptional performance on a specific attribute. Originality/value The authors demonstrate a consequential exception to the marketing wisdom that brands should extend to similar categories. Although the degree of brand extension similarity has been repeatedly shown to have a positive effect on brand extension evaluation, the authors document a case when its effect is actually detrimental. This study’s focus on the dependent variable of perceived performance on specific attributes is novel in the brand extension literature.
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Liu, Chaohui, Xianjin Kong, Xiang Li, and Tongxin Zhang. "Collaborative Filtering Recommendation Algorithm Based on User Attributes and Item Score." Scientific Programming 2022 (March 22, 2022): 1–7. http://dx.doi.org/10.1155/2022/4544152.
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To solve the problems of cold start and data sparseness existing in traditional collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on user attributes and item scoring is proposed. Firstly, we improve the credibility of user similarity and explore the potential interests of users, a new user rating similarity calculation method is constructed by introducing confidence, item popularity, and Pearson weighting. Secondly, we construct a user attribute similarity measurement method by introducing cultural distance, age attribute similarity, and user label similarity. Finally, user rating similarity and user attribute similarity are weighted to form a new similarity measurement model. Through simulation comparison between the collaborative filtering recommendation algorithm and the traditional recommendation algorithm, our results show that the collaborative filtering recommendation algorithm can effectively improve the accuracy of recommendations and the diversity of results and effectively alleviate the problem of data sparseness.
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Venturino, Michael, Nathan J. Romano, Sheryl L. Miller, Megan Murphy, and Tara M. Coffey. "Dynamic Memory: Keeping Track of Continually Changing Information." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 19 (1994): 1317–21. http://dx.doi.org/10.1177/154193129403801915.
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The requirement to remember continuously changing information places substantial demands on the human operator's working memory system. Previous research (Yntema & Mueser, 1960) found that in keeping track of dynamically changing information, humans' memory for changing information was better when they kept track of many different attributes of a single object than when they kept track of the identical attribute of many different objects. Due to a confound in the Yntema and Mueser experiment, the unique and combined effects of information organization and similarity-based interference cannot be determined, limiting the information about dynamic memory. This experiment represents an attempt to overcome this limitation by assessing the roles of organization and similarity-based interference in dynamic memory. The experimental task was a keeping track task in which a series of changing attribute values were presented sequentially, and subjects were required to remember the most recent update for each attribute. Three factors were manipulated in the experiment: number of “objects” (one vs many objects), type of attribute (same vs different), and memory load (2, 4, or 6 attributes to remember). Results showed that as memory load increased, keeping track performance in the many-object condition decreased to a greater extent than in the one-object condition. Also, as memory load increased, accuracy decreased at a greater rate for the same-attribute condition than for the different-attribute condition. The effect size for attribute similarity was much larger than that for number of objects. It was concluded that similarity-based interference is quite destructive to dynamic memory. It appears that the cost of attribute similarity far outweighs the benefits of organizing the continually-changing attributes. Such results have implications for structuring tasks and aiding memory in situations where operators must remember information in dynamically changing environments.
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Dewett, Dustin T., and Alissa A. Henza. "Spectral similarity fault enhancement." Interpretation 4, no. 1 (2016): SB149—SB159. http://dx.doi.org/10.1190/int-2015-0114.1.
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Fault interpretation in seismic data is a critical task that must be completed to thoroughly understand the structural history of the subsurface. The development of similarity-based attributes has allowed geoscientists to effectively filter a seismic data set to highlight discontinuities that are often associated with fault systems. Furthermore, there are numerous workflows that provide, to varying degrees, the ability to enhance this seismic attribute family. We have developed a new method, spectral similarity, to improve the similarity enhancement by integrating spectral decomposition, swarm intelligence, magnitude filtering, and orientated smoothing. In addition, the spectral similarity method has the ability to take any seismic attribute (e.g., similarity, curvature, total energy, coherent energy gradient, reflector rotation, etc.), combine it with the benefits of spectral decomposition, and create an accurate enhancement to similarity attributes. The final result is an increase in the quality of the similarity enhancement over previously used methods, and it can be computed entirely in commercial software packages. Specifically, the spectral similarity method provides a more realistic fault dip, reduction of noise, and removal of the discontinuous “stair-step” pattern common to similarity volumes.
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Kaseebhotla, Rajasekhar, K. Raghava Rao, and Mallikarjuna Rao. "Attribute Selection Algorithm with Clustering based Optimization Approach based on Mean and Similarity Distance." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8s (2023): 585–94. http://dx.doi.org/10.17762/ijritcc.v11i8s.7241.
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With hundreds or thousands of attributes in high-dimensional data, the computational workload is challenging. Attributes that have no meaningful influence on class predictions throughout the classification process increase the computing load. This article's goal is to use attribute selection to reduce the size of high-dimensional data, which will lessen the computational load. Considering selected attribute subsets that cover all attributes. As a result, there are two stages to the process: filtering out superfluous information and settling on a single attribute to stand in for a group of similar but otherwise meaningless characteristics. Numerous studies on attribute selection, including backward and forward selection, have been undertaken. This experiment and the accuracy of the categorization result recommend a k-means based PSO clustering-based attribute selection. It is likely that related attributes are present in the same cluster while irrelevant attributes are not identified in any clusters. Datasets for Credit Approval, Ionosphere, Annealing, Madelon, Isolet, and Multiple Attributes are employed alongside two other high-dimensional datasets. Both databases include the class label for each data point. Our test demonstrates that attribute selection using k-means clustering may be done to offer a subset of characteristics and that doing so produces classification outcomes that are more accurate than 80%.
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Greco-Vigorito, Carolyn. "Categorization Based on Attribute versus Relational Similarity in 4-To 10-Month-Old Infants." Perceptual and Motor Skills 82, no. 3 (1996): 915–27. http://dx.doi.org/10.2466/pms.1996.82.3.915.
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4- to 10-month-old infants were tested in 2 experiments to determine whether they used a similar attribute or a similar relationship among attributes to make visual judgments of similarity and categorization. In Exp. 1 infants were familiarized with a single stimulus composed of several attributes and a prescribed relationship among the attributes, left wing smaller than right wing. When tested in a novelty-preference procedure with novel stimuli that either preserved a single attribute but violated the relationship (Attribute Test Stimulus) or preserved the relationship with a new set of attributes (Relational Test Stimulus), 4-mo.-olds treated the Attribute Test Stimulus as familiar, whereas 6-mo.-olds treated the Relational Test Stimulus as familiar. Neither 8- nor 10-mo.-olds showed a preference for either test stimulus. In Exp. 2 a category containing 3 exemplars was constructed. In each exemplar a single attribute, left wing, was held constant, and all 3 exemplars shared the same relational structure, left wing smaller than right wing, but the remaining attributes varied across exemplars. Four-, 6-, and 8-mo.-olds in Exp 2 reliably included the novel Attribute Test Stimulus in the category. These data suggest that, although infants under 8 months can recognize relational information, they may not always use that information when making categorization judgments, particularly if a single, well-defined attribute is available as the basis for categorization.
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Zhou, Yan, and Yan-Ling Bao. "A Novel Attribute Reduction Algorithm for Incomplete Information Systems Based on a Binary Similarity Matrix." Symmetry 15, no. 3 (2023): 674. http://dx.doi.org/10.3390/sym15030674.
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With databases growing at an unrelenting rate, it may be difficult and complex to extract statistics by accessing all of the data in many practical problems. Attribute reduction, as an effective method to remove redundant attributes from massive data, has demonstrated its remarkable capability in simplifying information systems. In this paper, we concentrate on reducing attributes in incomplete information systems. We introduce a novel definition of a binary similarity matrix and present a method to calculate the significance of attributes in correspondence. Secondly, We develop a heuristic attribute reduction algorithm using a binary similarity matrix and attribute significance as heuristic knowledge. In addition, we use a numerical example to showcase the practicality and accuracy of the algorithm. In conclusion, we demonstrate through comparative analysis that our algorithm outperforms some existing attribute reduction methods.
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Ortal, Patricia, and Masato Edahiro. "Similarity Measure for Product Attribute Estimation." IEEE Access 8 (2020): 179073–82. http://dx.doi.org/10.1109/access.2020.3027023.
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Liao, Weihua, Daizhong Hou, and Weiguo Jiang. "An Approach for a Spatial Data Attribute Similarity Measure Based on Granular Computing Closeness." Applied Sciences 9, no. 13 (2019): 2628. http://dx.doi.org/10.3390/app9132628.
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This paper proposes a spatial data attribute similarity measure method based on granular computing closeness. This method uses the distance and membership degree of different index levels of spatial entities to measure the similarity of attributes. It not only reflects the degree of similarity of spatial entity types at different index levels but also reflects the integration similarity between spatial entity types under a comprehensive index. This method embodies the layered idea of granular computing and can provide a basis for spatial problem decision making and for spatial entity classification. Finally, the feasibility and applicability of the method are verified by taking the similarity measure of the land-use type attribute in Guangxi as an example.
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Kim, Jongwan. "A Similarity-Based Skyline Query Scheme Reflecting User Preferences." Academic Society for Appropriate Technology 11, no. 1 (2025): 23–30. https://doi.org/10.37675/jat.2025.00633.
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This study proposes a similarity-based skyline query method capable of quantitatively reflecting user preferences in multi-attribute recommendation environments. Traditional skyline approaches do not consider the relative importance of user preferences across attributes, and their computational cost increases exponentially as the number of dimensions grows, leading to performance degradation in filtering. To address these limitations, the proposed method applies MinMax normalization to user-preferred attributes—such as distance, rating, and grade—and calculates the Euclidean distance between these attributes and the user preference vector to obtain a single similarity score. This similarity score is then combined with price, an independent attribute, to form a two-dimensional attribute vector (price, similarity). Based on this vector, the skyline query is executed to select alternative objects that align with user preferences. Experimental results demonstrate that the proposed method reduces the number of recommended objects by approximately 80–90% compared to the traditional approach, while also reducing the total number of operations to less than half, thereby simplifying the comparison structure and lowering computational cost. By integrating user-centric preference modeling with a dimension-reduction-based comparison framework, this study presents a filtering mechanism that is highly applicable to recommendation systems and real-time decision support environments. As a result, it can serve as a competitive solution in user-personalized recommendation scenarios where both precision and real-time processing efficiency are required.
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Huang, Zhili, Qiang He, Qinglan Chen, and Hongge Yue. "Hamming Similarity Programming Model for Multi-Attribute Decision-Making Objects with Attribute Values as Interval Numbers and Its Application." Symmetry 14, no. 10 (2022): 2203. http://dx.doi.org/10.3390/sym14102203.
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With regard to the interval number-based uncertain multi-attribute decision making problem, in which the attribute weights are unknown and there is no preference on decision-making alternative objects, this paper presents a new decision-making approach. In this method, Hamming distance firstly is used to define the Hamming similarity degree of normative interval numbers, and the Hamming similarity degree of decision-making alternative objects, and then the Hamming similarity superiority relation theory to the comparison of interval numbers is proposed and some relevant results are obtained. Thus, by drawing on the idea of deviations maximization, an interval number-based decision-making object Hamming similarity programming model (IN-DMOHSPM) is established to calculate and solve the weight vector of attributes. Next, all of the selected alternative objects set is screened and sorted by using the overall Hamming similarity degree of each decision-making object compared with the ideal optimal object, and a new algorithm of Hamming similarity programming model for interval number-based multiple attribute decision-making objects is presented. Finally, the feasibility and utility of this model used in this paper are demonstrated by a numerical example.
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Zhang, Junming, Deli Wang, Bin Hu, and Xiangbo Gong. "An Automatic Velocity Analysis Method for Seismic Data-Containing Multiples." Remote Sensing 14, no. 21 (2022): 5428. http://dx.doi.org/10.3390/rs14215428.
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Normal moveout (NMO)-based velocity analysis can provide macro velocity models for prestack data processing and seismic attribute inversion. Datasets with an increasing size require conventional velocity analysis to be transformed to a more automatic mode. The sensitivity to multiple reflections limits the wide application of automatic velocity analysis. Thus, we propose an automatic velocity analysis method for seismic data-containing multiples to overcome the limit of multiple interference. The core idea of the proposed algorithm is to utilize a multi-attribute analysis system to transform the multiple attenuation problem to a multiple identification problem. To solve the identification problem, we introduce the local similarity to attribute the predicted multiples and build a quantitative attribute called multiple similarity. Considering robustness and accuracy, we select two supplementary attributes based on velocity and amplitude difference, i.e., velocity variation with depth and amplitude level. Then we utilize the technique for order preference by similarity to ideal solution (TOPSIS) to balance weights for different attributes in automatic velocity analysis. An RGB system is adopted for multi-attributes fusion in velocity spectra for visualization and quality control. Using both synthetic and field examples to evaluate the effectiveness of the proposed method for data-containing multiples, the results demonstrate the excellent performance in the accuracy of the extracted velocity model.
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S. Anitha and A. Francina Shalini. "Similarity Measure of Plithogenic Cubic Vague Sets: Examples and Possibilities." Neutrosophic Systems with Applications 11 (October 21, 2023): 39–47. http://dx.doi.org/10.61356/j.nswa.2023.81.
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The crisp, fuzzy, intuitionistic fuzzy, and neutrosophic sets are the extension of the plithogenic set, in which elements are characterized by the number of attributes and each attribute can assume many values. To achieve more accuracy and precise exclusion, a contradiction or dissimilarity degree is specified between each attribute and the values of the dominating attribute. A plithogenic cubic vague set is a combination of a plithogenic cubic set and a vague set. The key tool for resolving problems with pattern recognition and clustering analysis is the similarity measure. In this research, we characterize and investigate the similarities between two Plithogenic Cubic Vague sets (PCVSs) for (z≡F), (z≡IF) and (z≡N). Also, examples are given to examine similarities in the pattern recognition application problems.
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Liang, Wei, Zuo Chen, Ya Wen, and Weidong Xiao. "An Alert Fusion Method Based on Grey Relation and Attribute Similarity Correlation." International Journal of Online Engineering (iJOE) 12, no. 08 (2016): 25. http://dx.doi.org/10.3991/ijoe.v12i08.5958.
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Various security devices which produce a large volume of logs and alerts have been used widely. It is such a troublesome and time-consuming task for network managers to analyze and deal with the information. This paper presented an improved alerts aggregation method based on grey correlation and attribute similarity method. We used grey correlation to ascertain the importance of alert attributes in network security, and considered it as the weight of attributes. Then we combined with the attribute similarity method and calculated the overall feature similarity in order to complete alert aggregation. Experiments results showed that this method had a strict mathematical theory basis and a higher practical value, which can effectively reduce raw alerts and reduce redundancy for alert data fusion.
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Trisedya, Bayu Distiawan, Jianzhong Qi, and Rui Zhang. "Entity Alignment between Knowledge Graphs Using Attribute Embeddings." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 297–304. http://dx.doi.org/10.1609/aaai.v33i01.3301297.
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The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the semantic similarity between entities in the same knowledge graph. We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs. Our proposed model helps align entities from different knowledge graphs, and hence enables the integration of multiple knowledge graphs. Our model exploits large numbers of attribute triples existing in the knowledge graphs and generates attribute character embeddings. The attribute character embedding shifts the entity embeddings from two knowledge graphs into the same space by computing the similarity between entities based on their attributes. We use a transitivity rule to further enrich the number of attributes of an entity to enhance the attribute character embedding. Experiments using real-world knowledge bases show that our proposed model achieves consistent improvements over the baseline models by over 50% in terms of hits@1 on the entity alignment task.
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Pradana, Fajar, Bayu Priyambadha, and Denny Sagita Rusdianto. "Identifying Thresholds for Similarity-Based Class Cohesion (SCC) Metrics." Journal of Information Technology and Computer Science 1, no. 2 (2017): 72. http://dx.doi.org/10.25126/jitecs.20161213.
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Abstract. The object-oriented design (OOD) concept can be used to implement a quality measurement program is based on the possibility of inter-relationship between attributes and methods in the class diagram and interaction between objects on a communication diagram. The process of calculating the value of cohesion on the design of object-oriented software using Similarity-Based Class Cohesion metrics can be done by identifying the relationship between the three types of possible interaction between those methods, method-attribute, and interaction attribute-attribute. But the existence of such measurements theory is rarely used in the software development industry. This is due to there is no threshold value that is used as the limit of good or bad design. This study aims to determine the threshold of cohesion metric based on the class diagram. The result showed that the threshold of SCC metric is 0.45. 0.45 is the value that has the highest level of agreement with the design expert
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Ma, Zhe, Jianfeng Dong, Zhongzi Long, et al. "Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11741–48. http://dx.doi.org/10.1609/aaai.v34i07.6845.
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This paper strives to learn fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute among fashion items, which has potential values in many fashion related applications such as fashion copyright protection. To this end, we propose an Attribute-Specific Embedding Network (ASEN) to jointly learn multiple attribute-specific embeddings in an end-to-end manner, thus measure the fine-grained similarity in the corresponding space. With two attention modules, i.e., Attribute-aware Spatial Attention and Attribute-aware Channel Attention, ASEN is able to locate the related regions and capture the essential patterns under the guidance of the specified attribute, thus make the learned attribute-specific embeddings better reflect the fine-grained similarity. Extensive experiments on four fashion-related datasets show the effectiveness of ASEN for fine-grained fashion similarity learning and its potential for fashion reranking. Code and data are available at https://github.com/Maryeon/asen.
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Chen, Mengqi, Jingyang Xia, Ruoyun Huang, and Weiguo Fang. "Case-Based Reasoning System for Aeroengine Fault Diagnosis Enhanced with Attitudinal Choquet Integral." Applied Sciences 12, no. 11 (2022): 5696. http://dx.doi.org/10.3390/app12115696.
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As the core process of case-based reasoning (CBR), case retrieval is the foundation for CBR success, and the quality of case retrieval depends on the case similarity measure. We improved the CBR system for aeroengine fault diagnosis by embedding the attitudinal Choquet integral (ACI) and 2-order additive measure to consider attribute interactions and decision makers’ attitudes. The enhanced case retrieval method can not only integrate the local similarity, attribute importance, and interaction between attributes, but also incorporate the attitude of the decision maker, thus producing more comprehensive and reasonable global similarity and high-quality recommendations. An experimental study of aeroengine fault diagnosis and comparisons with other similarity aggregation methods were performed to demonstrate the effectiveness of the proposed method.
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Sun, Jianping, Hantao Cao, Biao Geng, Zhaoping Tang, and Xiaopeng Li. "Demand Prediction of Railway Emergency Resources Based on Case-Based Reasoning." Journal of Advanced Transportation 2021 (April 9, 2021): 1–10. http://dx.doi.org/10.1155/2021/6666631.
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The demand prediction of emergency resources is helpful for rational allocation and optimization of emergency resources for railway rescue when emergency incident occurs. In this paper, a case base containing China railway traffic accident that has occurred since 1978 is established, and the case-based reasoning (CBR) method is applied in railway emergency resource demand predicting research. The core case attributes of railway emergencies are described. In view of the attribute types of railway emergency cases, five types of attributes, including enumeration, numerical, interval, character and fuzzy type, are considered, and the local similarity calculation models of different attributes are given. In order to avoid the problem of missing attribute in the traditional nearest neighbor algorithm, a global case similarity calculation method based on structural similarity and attribute similarity is designed. The empirical results show that case 3 is the most similar to the target case, and the calculating quantities of the proposed model are closer to the actual usage quantity and more accurate in the demand prediction of railway emergency resources, compared with the traditional empirical method. The relative errors of demand forecasts for the 9 resources have been, respectively, reduced by 15.9884%, 15.1471%, 6.4286%, 17.1429%, 66.6667%, 38.8889%, 27.5%, 0%, and 17.7778%. Therefore, the proposed model is both reasonable and applicable. The research results are of great significance to effectively deal with railway emergencies.
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Kavousi Ghahfarokhi, Payam, and Thomas H. Wilson. "Fracture intensity attribute for the Tensleep reservoir at Teapot Dome, Wyoming, USA." Interpretation 3, no. 3 (2015): SZ41—SZ48. http://dx.doi.org/10.1190/int-2014-0258.1.
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The Tensleep oil reservoir at Teapot Dome, Wyoming, USA, is a naturally fractured tight sandstone reservoir that has been considered for carbon dioxide enhanced oil recovery ([Formula: see text]-EOR) and sequestration. Interpretation of open fractures identified in wireline image logs from the field suggests that the reservoir fracture network is dominated by early formed structural hinge-oblique fractures with interconnectivity enhanced by hinge-parallel and hinge-perpendicular fracture sets. Previous studies show that 3D seismic scale discontinuity attributes are dominated by more recent hinge-parallel and strike slip trends. The most negative curvature attribute that we used highlights concave features attributed to subtle traveltime delay through fracture zones and small faults or flexures associated with the fracture swarms. The poststack discontinuity extraction workflow incorporated seismic spectral blueing (SSB) to enhance the resolution of the seismic data. The SSB process is followed by computation of the short-wavelength most negative curvature. Subsequently, the minimum similarity attribute is applied to accentuate regions with minimum similarity of curvature. An edge-illumination process is then applied to the minimum similarity of the most negative curvature output. Discontinuities extracted through edge illumination locate regions of minimal similarity in curvature along fracture zones or small fault boundaries. This workflow enhances hinge-oblique discontinuities without azimuthal filtering and provides a fracture intensity attribute, which is used as an input to distribute the fracture intensity through the model discrete fracture network. Qualitative correlation of production data to extracted discontinuities suggests that wells located on hinge-oblique discontinuities are more productive than other wells in the field.
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Xie, Rui, Zhifeng Hao, and Bo Liu. "Integrating Entity and Attribute for Object Similarity." Open Automation and Control Systems Journal 7, no. 1 (2015): 398–403. http://dx.doi.org/10.2174/1874444301507010398.
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Wang, Can, Xiangjun Dong, Fei Zhou, Longbing Cao, and Chi-Hung Chi. "Coupled Attribute Similarity Learning on Categorical Data." IEEE Transactions on Neural Networks and Learning Systems 26, no. 4 (2015): 781–97. http://dx.doi.org/10.1109/tnnls.2014.2325872.
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Ma, Wenchao, Charles Iaconangelo, and Jimmy de la Torre. "Model Similarity, Model Selection, and Attribute Classification." Applied Psychological Measurement 40, no. 3 (2016): 200–217. http://dx.doi.org/10.1177/0146621615621717.
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Hasanzadeh Saray, Marzieh, Seyed Saeid Eslamian, Björn Klöve, and Alireza Gohari. "Regionalization of potential evapotranspiration using a modified region of influence." Theoretical and Applied Climatology 140, no. 1-2 (2019): 115–27. http://dx.doi.org/10.1007/s00704-019-03078-2.
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AbstractThis study examined the effect of different attributes on regionalization of potential evapotranspiration (ETp) in Urmia Lake Basin (ULB), Iran, using the region of influence (RoI) framework. Data for the period 1997–2016 from 30 weather stations were selected for the analysis. To achieve similarity between stations, climate, geographical, and statistical attributes were selected. To determine the effect of each attribute, the Shannon entropy weighting method was used. The results showed that attribute weighting had a significant impact on ETp clustering. Among the groups studied, the most significant effect of weighting was observed in the statistical attributes category. Among all attributes, skewness coefficient (CS) was the most useful in determining similarity between stations. Based on the results, ULB can be divided into three homogeneous regions. Proximity of weather stations did not always indicate similarity between them, but by weighting the stations in addition to weighting the attributes, more accurate estimates of ETp in the basin were obtained. Overall, the results demonstrate potential for application of the RoI approach in regionalization of ETp, by assigning a weight to weather stations and to influencing attributes.
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Li, Guo Qi, and Si Jing Liu. "The Calculation Method of Similarity Degree for City Logistics Facilities." Advanced Materials Research 219-220 (March 2011): 1621–24. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1621.
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For the multi-attribute characteristics of scale, quantity, service radius and target of service in city logistics facilities，this paper considered the similar phenomena between city logistics facilities caused by the interaction of different social and economic attributes. Based on the analysis of the similarity degree calculation methods in computer science and mechanical engineering, it proposed two calculation methods of similarity degree in city logistics facilities. The qualitative and quantitative attributes were considered separately in the first method, the quantitative attributes were disposed by triangular fuzzy number.The similarity dimension was introduced as the basis of the similarity degree calculation in the second method. A merge processing method was used to incorporate all similar characteristics of every similarity dimension and a similarity calculation formula was deduced from the theory of similarity.
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Li, Zhen Dong, and Fei Li. "A Clustering Algorithm Based on Variance-Similarity." Applied Mechanics and Materials 333-335 (July 2013): 1306–9. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1306.
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Clustering algorithms, like K-means Algorithm, use distances in attribute space to cluster data. However the computation of distances in attribute space influences the accuracy. So innovatively, Variance-Similarity clustering algorithm defines similarity as a function of the attribute variance, and clusters data by the comparison of similarities. In computer simulation, the comparison of Variance-Similarity Algorithm and K-means Algorithm on UCI data sets presents that Variance-Similarity Algorithm has a better clustering accuracy than K-means Algorithm.
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Yang, Xiaohui, and Ying Sun. "User Audit Model Based on Attribute Measurement and Similarity Measurement." Security and Communication Networks 2020 (March 9, 2020): 1–9. http://dx.doi.org/10.1155/2020/8387672.
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The Internet of Things (IoT) is an open network. And, there are a large number of malicious nodes in the network. These malicious nodes may tamper with the correct data and pass them to other nodes. The normal nodes will use the wrong data for information dissemination due to a lack of ability to verify the correctness of the messages received, resulting in the dissemination of false information on medical, social, and other networks. Auditing user attributes and behavior information to identify malicious user nodes is an important way to secure networks. In response to the user nodes audit problem, a user audit model based on attribute measurement and similarity measurement (AM-SM-UAM) is proposed. Firstly, the user attribute measurement algorithm is constructed, using a hierarchical decision model to construct a judgment matrix to analyze user attribute data. Secondly, the blog similarity measurement algorithm is constructed, evaluating the similarity of blog posts published by different users based on the improved Levenshtein distance. Finally, a user audit model based on a security degree is built, and malicious users are defined by security thresholds. Experimental results show that this model can comprehensively analyze the attribute and behavior data of users and have more accurate and stable performance in the practical application of the network platforms.
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Kim, Yejin, and Jongik Kim. "Efficient Similarity Search of Multi-Attribute Records using An Optimal Attribute Assignment." Journal of KIISE 46, no. 2 (2019): 193–201. http://dx.doi.org/10.5626/jok.2019.46.2.193.
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Bardhi, Arjada. "Attributes: Selective Learning and Influence." Econometrica 92, no. 2 (2024): 311–53. http://dx.doi.org/10.3982/ecta18355.
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An agent selectively samples attributes of a complex project so as to influence the decision of a principal. The players disagree about the weighting, or relevance, of attributes. The correlation across attributes is modeled through a Gaussian process, the covariance function of which captures pairwise attribute similarity. The key trade‐off in sampling is between the alignment of the players' posterior values for the project and the variability of the principal's decision. Under a natural property of the attribute correlation—the nearest‐attribute property (NAP)—each optimal attribute is relevant for some player and at most two optimal attributes are relevant for only one player. We derive comparative statics in the strength of attribute correlation and examine the robustness of our findings to violations of NAP for a tractable class of distance‐based covariances. The findings carry testable implications for attribute‐based product evaluation and strategic selection of pilot sites.
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Yin, Chuan, Binyu Zhang, Wanzeng Liu, et al. "Geographic Knowledge Graph Attribute Normalization: Improving the Accuracy by Fusing Optimal Granularity Clustering and Co-Occurrence Analysis." ISPRS International Journal of Geo-Information 11, no. 7 (2022): 360. http://dx.doi.org/10.3390/ijgi11070360.
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Expansion of the entity attribute information of geographic knowledge graphs is essentially the fusion of the Internet’s encyclopedic knowledge. However, it lacks structured attribute information, and synonymy and polysemy always exist. These reduce the quality of the knowledge graph and cause incomplete and inaccurate semantic retrieval. Therefore, we normalize the attributes of a geographic knowledge graph based on optimal granularity clustering and co-occurrence analysis, and use structure and the semantic relation of the entity attributes to identify synonymy and correlation between attributes. Specifically: (1) We design a classification system for geographic attributes, that is, using a community discovery algorithm to classify the attribute names. The optimal clustering granularity is identified by the marker target detection algorithm. (2) We complete the fine-grained identification of attribute relations by analyzing co-occurrence relations of the attributes and rule inference. (3) Finally, the performance of the system is verified by manual discrimination using the case of “landscape, forest, field, lake and grass”. The results show the following: (1) The average precision of spatial relations was 0.974 and the average recall was 0.937; the average precision of data relations was 0.977 and the average recall was 0.998. (2) The average F1 for similarity results is 0.473; the average F1 for co-occurrence analysis results is 0.735; the average F1 for rule-based modification results is 0.934; the results show that the accuracy is greater than 90%. Compared to traditional methods only focusing on similarity, the accuracy of synonymous attribute recognition improves the system and we are capable of identifying near-sense attributes. Integration of our system and attribute normalization can greatly improve both the processing efficiency and accuracy.
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Wang, Zhenxin, Tao Liu, Yujie Wang, et al. "Graph-Clustering Anonymity Privacy Protection Algorithm With Fused Distance-Attributes." Journal of Physics: Conference Series 2504, no. 1 (2023): 012058. http://dx.doi.org/10.1088/1742-6596/2504/1/012058.
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Abstract Clustering anonymity is a common social network data privacy protection scheme, which is based on graph-clustering. Many existing graph clustering methods mainly focus on the relationship between the structure and attributes of nodes, and the difference between them due to the metric usually causes the problem of poor clustering results. To address the shortcomings in the above graph-clustering methods, a graph-clustering anonymity method implemented with fused distance-attributes (GCA-DA) is proposed. Firstly, the algorithm quantifies the distance and attribute similarity between nodes separately and balances the metric differences between them to calculate the integrated similarity. Then all the nodes in the graph are clustered into clusters according to the integrated similarity between two nodes, each of which contains no fewer than k nodes. Finally, all the clusters are anonymized. In this method, the attribute generalization for every cluster can prevent attacks by the background knowledge of structure and attributes. In addition, the attributes are divided into numerical and non-numerical attributes to measure them separately, therefore can maintain the usability of the data better. Experiment results demonstrate the effectiveness in improving the quality of clustering and reducing information loss.
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Xiao, Huimin, Liu Wang, and Chunsheng Cui. "Research on emergency management of urban waterlogging based on similarity fusion of multi-source heterogeneous data." PLOS ONE 17, no. 7 (2022): e0270925. http://dx.doi.org/10.1371/journal.pone.0270925.
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Global warming has seriously affected the local climate characteristics of cities, resulting in the frequent occurrence of urban waterlogging with severe economic losses and casualties. Aiming to improve the effectiveness of disaster emergency management, we propose a novel emergency decision model embedding similarity algorithms of heterogeneous multi-attribute based on case-based reasoning. First, this paper establishes a multi-dimensional attribute system of urban waterlogging catastrophes cases based on the Wuli-Shili-Renli theory. Due to the heterogeneity of attributes of waterlogging cases, different algorithms to measure the attribute similarity are designed for crisp symbols, crisp numbers, interval numbers, fuzzy linguistic variables, and hesitant fuzzy linguistic term sets. Then, this paper combines the best-worst method with the maximal deviation method for a more reasonable weight allocation of attributes. Finally, the hybrid similarity between the historical and the target cases is obtained by aggregating attribute similarities via the weighted method. According to the given threshold value, a similar historical case set is built whose emergency measures are used to provide the reference for the target case. Additionally, a case of urban waterlogging emergency is conducted to demonstrate the applicability and effectiveness of the proposed model, which exploits historical experiences and retrieves the optimal scheme for the current disaster emergency with heterogeneous multi attributes. Consequently, the proposed model solves the problem of diverse data types to satisfy the needs of case presentation and retrieval. Compared with the existing model, it can better realize the multi-dimensional expression and fast matching of the cases.
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Natsume, Hiroharu, Shogo Okamoto, and Hikaru Nagano. "TDS Similarity: Outlier Analysis Using a Similarity Index to Compare Time-Series Responses of Temporal Dominance of Sensations Tasks." Foods 12, no. 10 (2023): 2025. http://dx.doi.org/10.3390/foods12102025.
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Temporal dominance of sensations (TDS) methods are used to record temporally developing sensations while eating food samples. Results of TDS tasks are typically discussed using averages across multiple trials and panels, and few methods have been developed to analyze differences between individual trials. We defined a similarity index between two time-series responses of TDS tasks. This index adopts a dynamic level to determine the importance of the timing of attribute selection. With a small dynamic level, the index focuses on the duration for attributes to be selected rather than on the timing of the attribute selection. With a large dynamic level, the index focuses on the temporal similarity between two TDS tasks. We performed an outlier analysis based on the developed similarity index using the results of TDS tasks performed in an earlier study. Certain samples were categorized as outliers irrespective of the dynamic level, whereas the categorization of a few samples depended on the level. The similarity index developed in this study achieved individual analyses of TDS tasks, including outlier detection, and adds new analysis techniques to TDS methods.
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Chen, Qingxiao, Xuesi Zhou, Ji Wu, and Yongsheng Zhou. "Structuring electronic dental records through deep learning for a clinical decision support system." Health Informatics Journal 27, no. 1 (2021): 146045822098003. http://dx.doi.org/10.1177/1460458220980036.
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Extracting information from unstructured clinical text is a fundamental and challenging task in medical informatics. Our study aims to construct a natural language processing (NLP) workflow to extract information from Chinese electronic dental records (EDRs) for clinical decision support systems (CDSSs). We extracted attributes, attribute values, and tooth positions based on an existing ontology from EDRs. A workflow integrating deep learning with keywords was constructed, in which vectors representing texts were unsupervised learned. Specifically, we implemented Sentence2vec to learn sentence vectors and Word2vec to learn word vectors. For attribute recognition, we calculated similarity values among sentence vectors and extracted attributes based on our selection strategy. For attribute value recognition, we expanded the keyword database by calculating similarity values among word vectors to select keywords. Performance of our workflow with the hybrid method was evaluated and compared with keyword-based method and deep learning method. In both attribute and value recognition, the hybrid method outperforms the other two methods in achieving high precision (0.94, 0.94), recall (0.74, 0.82), and F score (0.83, 0.88). Our NLP workflow can efficiently structure narrative text from EDRs, providing accurate input information and a solid foundation for further data-based CDSSs.
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Ren, Shedong, Fangzhi Gui, Yanwei Zhao, Min Zhan, and Wanliang Wang. "An effective similarity determination model for case-based reasoning in support of low-carbon product design." Advances in Mechanical Engineering 12, no. 10 (2020): 168781402097031. http://dx.doi.org/10.1177/1687814020970313.
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In the initial stage of low-carbon product design, design information is always uncertain and incomplete, as well as the coupling properties between design attributes, thus it requires retrospective coordination for design conflicts resulting from the inclusion of low-carbon requirements. Reusing the prior design knowledge can promote design efficiency, however, the acquisition of similar cases knowledge not only needs to consider the similarity of design problems, but also the adaptability of candidate cases. This study presents an effective similarity determination model to support low-carbon product design, and targets of the proposed model are (1) to reasonably determine design ranges of attribute values for product cases retrieval by representing the uncertain design attributes with fuzzy set theory; (2) to construct an efficient indexing structure to generate the index set of similar cases based on the improved discretized highest similarity method by proposing two effective strategies; and, (3) to establish similarity estimation models for different types of attributes, and it calculates the information content of each attribute to evaluate the adaptability of cases based on the Information Axiom. The applicability of the proposed model is demonstrated through a case study of similar cases retrieval for the vacuum pump low-carbon design.
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Allroggen, Niklas, and Jens Tronicke. "Attribute-based analysis of time-lapse ground-penetrating radar data." GEOPHYSICS 81, no. 1 (2016): H1—H8. http://dx.doi.org/10.1190/geo2015-0171.1.
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Analysis of time-lapse ground-penetrating radar (GPR) data can provide information regarding subsurface hydrological processes, such as preferential flow. However, the analysis of time-lapse data is often limited by data quality; for example, for noisy input data, the interpretation of difference images is often difficult. Motivated by modern image-processing tools, we have developed two robust GPR attributes, which allow us to distinguish amplitude (contrast similarity) and time-shift (structural similarity) variations related to differences between individual time-lapse GPR data sets. We tested and evaluated our attributes using synthetic data of different complexity. Afterward, we applied them to a field data example, in which subsurface flow was induced by an artificial rainfall event. For all examples, we identified our structural similarity attribute to be a robust measure for highlighting time-lapse changes also in data with low signal-to-noise ratios. We determined that our new attribute-based workflow is a promising tool to analyze time-lapse GPR data, especially for imaging subsurface hydrological processes.
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Al Karomi, M. Adib, and Ivandari Ivandari. "Fuzzy Integration to Standard Calculation of K-Nearest Neighbour Attributes." JAICT 5, no. 2 (2020): 13. http://dx.doi.org/10.32497/jaict.v5i2.1984.
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<p class="AbstractL-MAG">The development of information and data in the era of the industrial revolution 4.0 is very fast. Researchers, institutions and even industry are competing to find and utilize methods in data processing that are more effective and efficient. In data mining classification, there are several best methods and are widely used by researchers. One of them is K-Nearest Neighbor (KNN). The calculation process in the KNN algorithm is carried out by comparing the testing data to all existing training data. This comparison is generally symbolized by the value of closeness or similarity between attribute records. The KNN method is proven to be good for handling large datasets and datasets with many attributes. One of the drawbacks in calculating the similarity of the KNN is that if there are attributes with a large range value, the similarity value will also be large. Conversely, if the range in an attribute is small, the similarity is also small. This condition is clearly unfair considering the types of attributes in the current data vary widely. One solution to this problem is to use standardization for all existing data attributes. Fuzzy is a model introduced by Prof. Zadeh which allows a faint value to be a value between 1 and 0. In this study the fuzzy model will be integrated in the KNN similarity calculation to obtain standardization of all data attributes. The results show that the use of the KNN algorithm in the classification of credit approval has an accuracy rate of 91.83%.</p>
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Ciobanu, Gabriel, and Cristian Văideanu. "Similarity relations in fuzzy attribute-oriented concept lattices." Fuzzy Sets and Systems 275 (September 2015): 88–109. http://dx.doi.org/10.1016/j.fss.2014.12.011.
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Boobalan, M. Parimala, Daphne Lopez, and X. Z. Gao. "Graph clustering using k-Neighbourhood Attribute Structural similarity." Applied Soft Computing 47 (October 2016): 216–23. http://dx.doi.org/10.1016/j.asoc.2016.05.028.
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Venturincv, Michael. "Interference and Information Organization in Keeping Track of Continually Changing Information." Human Factors: The Journal of the Human Factors and Ergonomics Society 39, no. 4 (1997): 532–39. http://dx.doi.org/10.1518/001872097778667942.
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The influences of information organization and similarity-based interference on memory for changing information were investigated in the present experiment. Participants performed a keeping-track task in which they had to remember the most recent value for each of several continually changing attributes associated with one or several objects. Recall was poor when participants kept track of the same changing attribute compared with when they kept track of different changing attributes. This pattern was observed whether many attributes were mapped to one object or a single attribute was mapped to many objects. Keeping-track performance also deteriorated as the number of information events intervening between presentation and recall increased. The results are discussed in terms of similarity-based interference. Also discussed is the notion that this dynamic task gives rise to the need to distinguish between memory capacity for static information and memory capacity for dynamically changing information.
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Venturino, Michael, Sheryl L. Miller, Karenza A. Ercolano, and Keith Josephson. "Interference and Information Organization in Dynamic Memory." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 39, no. 21 (1995): 1430–33. http://dx.doi.org/10.1177/154193129503902115.
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Two experiments were performed in order to explore the relationship between two memory phenomena that determine one's ability to keep track of continually changing information: attribute similarity and information organization. In Experiment 1, attribute similarity was minimized while information organization was varied. Results showed that the memory requirements involved in maintaining numerous object-attribute associations (i.e., grouping) did not hinder subjects’ ability to successfully retrieve information, but did affect temporal aspects of the retrieval process itself. In Experiment 2, information organization was varied under conditions of greater attribute similarity. In the presence of substantial information similarity, information organization had a beneficial effect, allowing for greater recall accuracy when the information could be meaningfully grouped, while also not incurring a temporal cost. The type of retrieval errors were also classified and discussed.
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Hu, Jian, Hong Li, and Jin-hua Sun. "Research on Case Reasoning Multi-attribute Group Decision-making Method Based on Hesitant Fuzzy Set." Research in Economics and Management 9, no. 1 (2024): p187. http://dx.doi.org/10.22158/rem.v9n1p187.
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In this study, a hesitant fuzzy set-based case-based reasoning integration method is proposed for the multi-attribute group decision-making problem with unknown attribute weights and mixed forms of attribute values. First, from two perspectives, traditional distance measure and information theory, a multi-objective optimization model is constructed using the distance similarity measure and information entropy of each type of attributes to determine the attribute weights. Secondly, considering the hybrid and nonlinear characteristics of case data, based on the principle of symmetric interaction entropy and TOPSIS method, a global similarity measure based on symmetric interaction entropy is proposed and a case inference algorithm suitable for hesitant fuzzy environment is designed. Finally, by analyzing the arithmetic cases of the target case in the case base, the most similar historical cases to the target case are retrieved to determine the decision-making scheme, and the practicality and feasibility of the decision-making method are verified. The results show that considering hesitant fuzzy theory for case-based reasoning research will help improve the accuracy and reliability of decision-making and provide more effective support for multi-attribute group decision management.
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Boobalan, Parimala M. "Grouping of Nodes in Social Networks Based on Multiphase Approach." Recent Patents on Computer Science 12, no. 1 (2019): 25–33. http://dx.doi.org/10.2174/2213275911666181022111924.
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Background: Recent advances in the field of information and social network has led to the problem of community detection that has got much attention among the researchers. Objective: This paper focus on community discovery, a fundamental task in network analysis by balancing both attribute and structural similarity. The attribute similarity is evaluated using the Jaccard coefficient and Structural similarity is achieved through modularity. Methods: The proposed algorithm is designed for identifying communities in social networks by fusing attribute and structural similarity. The algorithm retains the node which has high influence on the other nodes within the neighbourhood and subsequently groups the objects based on the similarity of the information among the nodes. The extensive analysis is performed on real world datasets like Facebook, DBLP, Twitter and Flickr with different sizes that demonstrates the effectiveness and efficiency of the proposed algorithm over the other algorithms. Results: The results depicts that the generated clusters have a good balance between the structural and attribute with high intracluster similarity and less intracluster similarity. The algorithm helps to achieve faster runtime for moderately-sized datasets and better runtime for large datasets with superior clustering quality.