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Journal articles on the topic 'Clustering-based approach'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Feng, Yuxiang, Simon Pickering, Edward Chappell, Pejman Iravani, and Chris Brace. "A Support Vector Clustering Based Approach for Driving Style Classification." International Journal of Machine Learning and Computing 9, no. 3 (2019): 344–50. http://dx.doi.org/10.18178/ijmlc.2019.9.3.808.

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2

Lubbad, Mohammed A. H., and Wesam M. Ashour. "Cosine-Based Clustering Algorithm Approach." International Journal of Intelligent Systems and Applications 4, no. 1 (2012): 53–63. http://dx.doi.org/10.5815/ijisa.2012.01.07.

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3

Ahmed, Soraya Setti, Mimoun Malki, and Sidi Mohamed Benslimane. "Ontology Partitioning: Clustering Based Approach." International Journal of Information Technology and Computer Science 7, no. 6 (2015): 1–11. http://dx.doi.org/10.5815/ijitcs.2015.06.01.

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4

Kaur, Supreet. "Quality Prediction of Object Oriented Software Using Density Based Clustering Approach." International Journal of Engineering and Technology 3, no. 4 (2011): 440–45. http://dx.doi.org/10.7763/ijet.2011.v3.267.

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5

Jayaneththi, J. K. D. B. G., and Banage T. G. S. Kumara. "Clustering-Based Approach for Clustering Journals in Computer Science." International Journal of Systems and Service-Oriented Engineering 9, no. 2 (2019): 35–51. http://dx.doi.org/10.4018/ijssoe.2019040103.

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In the present scientific world, most of the authors of scientific literature are seeking effective ways to share their research findings with large peer groups. But finding a high-quality journal to publish paper is a huge challenge. Most of the journals present today are predatory and less-quality. The main aim of this study is to help the researchers in identifying the quality level of computer science journals by introducing a data mining model based on six journal quality metrics (Journal Impact Factor, SCImago Journal Rank, Eigenfactor, H-index, Source Normalized Impact per Paper, and Ar

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6

Kampffmeyer, Michael, Sigurd Løkse, Filippo M. Bianchi, Lorenzo Livi, Arnt-Børre Salberg, and Robert Jenssen. "Deep divergence-based approach to clustering." Neural Networks 113 (May 2019): 91–101. http://dx.doi.org/10.1016/j.neunet.2019.01.015.

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7

Yuan Huang, and Bing Li. "A Similarity-Based Service Clustering Approach." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 21 (2012): 613–21. http://dx.doi.org/10.4156/aiss.vol4.issue21.78.

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8

Laloë, Thomas. "Quantization based clustering: An iterative approach." Pattern Recognition Letters 142 (February 2021): 51–57. http://dx.doi.org/10.1016/j.patrec.2020.12.007.

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9

Ahmadi, Abbas, Fakhri Karray, and Mohamed S. Kamel. "Flocking based approach for data clustering." Natural Computing 9, no. 3 (2009): 767–91. http://dx.doi.org/10.1007/s11047-009-9173-5.

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10

Zhang, De-gan, Hong-li Niu, and Si Liu. "Novel PEECR-based clustering routing approach." Soft Computing 21, no. 24 (2016): 7313–23. http://dx.doi.org/10.1007/s00500-016-2270-3.

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11

Liu, Gang, Nan Qi, Jiaxin Chen, Chao Dong, and Zanqi Huang. "Enhancing clustering stability in VANET: A spectral clustering based approach." China Communications 17, no. 4 (2020): 140–51. http://dx.doi.org/10.23919/jcc.2020.04.013.

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12

Sharma, Ashish, and Anjani Rai. "An Approach of Leading Sequence Clustering (LSC) Algorithm based Scheduling and Agglomerative Mean Shift Clustering for Load Balancing in Cloud." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 618–24. http://dx.doi.org/10.5373/jardcs/v11sp10/20192850.

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13

Mittal, Mamta, Rajendra Kumar Sharma, Varinder Pal Singh, and Raghvendra Kumar. "Adaptive Threshold Based Clustering." International Journal of Information System Modeling and Design 10, no. 1 (2019): 42–59. http://dx.doi.org/10.4018/ijismd.2019010103.

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Partitioning-based clustering methods have various challenges especially user-defined parameters and sensitivity to initial seed selections. K-means is most popular partitioning based method while it is sensitive to outlier, generate non-overlap cluster and non-deterministic in nature due to its sensitivity to initial seed selection. These limitations are regarded as promising research directions. In this study, a deterministic approach which do not requires user defined parameters during clustering; can generate overlapped and non-overlapped clusters and detect outliers has been proposed. Her

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14

Lee, Taehoon, Seung Jean Kim, Eui-Young Chung, and Sungroh Yoon. "K-maximin clustering: a maximin correlation approach to partition-based clustering." IEICE Electronics Express 6, no. 17 (2009): 1205–11. http://dx.doi.org/10.1587/elex.6.1205.

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15

WANG, Zhi-Hui, Jian XU, Wei WANG, and Bai-Le SHI. "A Clustering-Based Approach for Data Anonymization." Journal of Software 21, no. 4 (2010): 680–93. http://dx.doi.org/10.3724/sp.j.1001.2010.03508.

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16

Jain, Aaditya, and Suchita Tyagi. "Priority Based New Approach for Correlation Clustering." International Journal of Information Technology and Computer Science 9, no. 3 (2017): 71–79. http://dx.doi.org/10.5815/ijitcs.2017.03.08.

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17

GUI, Qiong, and Xiaohui CHENG. "Clustering-based approach for multi-level anonymization." Journal of Computer Applications 33, no. 2 (2013): 412–16. http://dx.doi.org/10.3724/sp.j.1087.2013.00412.

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18

Srivastava, Smriti, and Anant Kumar Jaiswal. "Clustering based Load Balanced Gateway Placement Approach." International Journal of Computer Applications 63, no. 5 (2013): 6–10. http://dx.doi.org/10.5120/10460-5171.

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19

Mascioli, F. M. Frattale, A. Rizzi, M. Panella, and G. Martinelli. "Scale-based approach to hierarchical fuzzy clustering." Signal Processing 80, no. 6 (2000): 1001–16. http://dx.doi.org/10.1016/s0165-1684(00)00016-5.

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20

Kumar, Yugal, Sumit Gupta, and G. Sahoo. "A Clustering Approach Based on Charged Particles." International Journal of Software Engineering and Its Applications 10, no. 3 (2016): 9–28. http://dx.doi.org/10.14257/ijseia.2016.10.3.02.

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21

Chen, Xue-wen, and Thomas Huang. "Facial expression recognition: A clustering-based approach." Pattern Recognition Letters 24, no. 9-10 (2003): 1295–302. http://dx.doi.org/10.1016/s0167-8655(02)00371-9.

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22

Zhang, Huaxiang, and Linlin Cao. "A spectral clustering based ensemble pruning approach." Neurocomputing 139 (September 2014): 289–97. http://dx.doi.org/10.1016/j.neucom.2014.02.030.

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23

Gemici Gunes, Ozlem, and A. Sima Uyar. "Parallelization of an ant‐based clustering approach." Kybernetes 39, no. 4 (2010): 656–77. http://dx.doi.org/10.1108/03684921011036844.

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24

Zhang, Caili, Takato Tatsumi, Masaya Nakata, and Keiki Takadama. "Approach to Clustering with Variance-Based XCS." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 5 (2017): 885–94. http://dx.doi.org/10.20965/jaciii.2017.p0885.

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This paper presents an approach to clustering that extends the variance-based Learning Classifier System (XCS-VR). In real world problems, the ability to combine similar rules is crucial in the knowledge discovery and data mining field. Conventionally, XCS-VR is able to acquire generalized rules, but it cannot further acquire more generalized rules from these rules. The proposed approach (called XCS-VRc) accomplishes this by integrating similar generalized rules. To validate the proposed approach, we designed a bench-mark problem to examine whether XCS-VRc can cluster both the generalized and

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25

Kale, Sudhir H. "Grouping Euroconsumers: A Culture-Based Clustering Approach." Journal of International Marketing 3, no. 3 (1995): 35–48. http://dx.doi.org/10.1177/1069031x9500300304.

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The move toward European integration entails significant adjustments in a firm's marketing strategy. Despite the dismantling of legislative barriers, cultural milieu of the various member nations will continue to be an obstacle toward true integration. This study clusters 17 European nations on Hofstede's (1980) cultural dimensions. Three distinct clusters emerge, each with its unique cultural composition. Scrutinizing the cultural make-up of each cluster offers the possibility of selecting an appropriate marketing mix across nations belonging to the same cluster.

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26

Yoshida, Tetsuya. "A GRAPH-BASED APPROACH FOR SEMISUPERVISED CLUSTERING." Computational Intelligence 30, no. 2 (2012): 263–84. http://dx.doi.org/10.1111/j.1467-8640.2012.00450.x.

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27

Kulev, Igor, Pearl Pu, and Boi Faltings. "A Bayesian Approach to Intervention-Based Clustering." ACM Transactions on Intelligent Systems and Technology 9, no. 4 (2018): 1–23. http://dx.doi.org/10.1145/3156683.

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28

Huang, Zhenhua, Yang Xiang, Bo Zhang, and Xiaoling Liu. "A clustering based approach for skyline diversity." Expert Systems with Applications 38, no. 7 (2011): 7984–93. http://dx.doi.org/10.1016/j.eswa.2010.12.104.

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29

AlSuwaidan, Lulwah, and Mourad Ykhlef. "Interest-Based Clustering Approach for Social Networks." Arabian Journal for Science and Engineering 43, no. 2 (2017): 935–47. http://dx.doi.org/10.1007/s13369-017-2800-z.

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30

Sridhar, V., and M. Narasimha Murty. "Knowledge-based clustering approach for data abstraction." Knowledge-Based Systems 7, no. 2 (1994): 103–13. http://dx.doi.org/10.1016/0950-7051(94)90023-x.

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31

Deng, Liang, Yueqing Wang, Cheng Chen, Yang Liu, Fang Wang, and Jie Liu. "A clustering-based approach to vortex extraction." Journal of Visualization 23, no. 3 (2020): 459–74. http://dx.doi.org/10.1007/s12650-020-00636-z.

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32

Bimali, Milan, and Khimraj Shrestha. "WEIGHTED LIKELIHOOD-BASED APPROACH TO HIERARCHICAL CLUSTERING." Advances and Applications in Statistics 66, no. 2 (2021): 209–26. http://dx.doi.org/10.17654/as066020209.

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33

Wang, Lianhui, Pengfei Chen, Linying Chen, and Junmin Mou. "Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach." Journal of Marine Science and Engineering 9, no. 6 (2021): 566. http://dx.doi.org/10.3390/jmse9060566.

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The Automatic Identification System (AIS) of ships provides massive data for maritime transportation management and related researches. Trajectory clustering has been widely used in recent years as a fundamental method of maritime traffic analysis to provide insightful knowledge for traffic management and operation optimization, etc. This paper proposes a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has

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34

Hess, Sibylle, Wouter Duivesteijn, Philipp Honysz, and Katharina Morik. "The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3788–95. http://dx.doi.org/10.1609/aaai.v33i01.33013788.

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When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporating these paradigms are Spectral Clustering and DBSCAN. Both paradigms have their pros and cons. While minimum cut clusterings are sensitive to noise, density-based clusterings have trouble handling clusters with varying densities. In this paper, we propose SPECTACL: a method combining the advantages of both approaches, while solving the two mentioned drawbacks. Our method is easy to implement, such as Spectral Cluster

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35

Hendalianpour, Ayad, Jafar Razmi, and Mohsen Gheitasi. "Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach." Accounting 3, no. 2 (2017): 81–94. http://dx.doi.org/10.5267/j.ac.2016.8.003.

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36

Rouba, Baroudi, and Safia Nait Bahloul. "A Multicriteria Clustering Approach Based on Similarity Indices and Clustering Ensemble Techniques." International Journal of Information Technology & Decision Making 13, no. 04 (2014): 811–37. http://dx.doi.org/10.1142/s0219622014500631.

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This paper deals with the problem of multicriteria clusters construction. The aim is to propose a multicriteria clustering procedure aiming at discovering data structures from a multicriteria perspective by defining a dissimilarity measure which takes into account the multicriteria nature of the problem. Comparing two objects in the multicriteria context is based on the preference information that expresses whether these objects are indifferent, incomparable or one is preferred to the other. The proposed approach uses this preference information with an agreement–disagreement similarity index

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37

Chen, Runzi, Shuliang Zhao, and Meishe Liang. "A Fast Multiscale Clustering Approach Based on DBSCAN." Wireless Communications and Mobile Computing 2021 (July 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/4071177.

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Multiscale brings great benefits for people to observe objects or problems from different perspectives. It has practical significance for clustering on multiscale data. At present, there is a lack of research on the clustering of large-scale data under the premise that clustering results of small-scale datasets have been obtained. If one does cluster on large-scale datasets by using traditional methods, two disadvantages are as follows: (1) Clustering results of small-scale datasets are not utilized. (2) Traditional method will cause more running overhead. Aims at these shortcomings, this pape

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38

MENÉNDEZ, HÉCTOR D., DAVID F. BARRERO, and DAVID CAMACHO. "A GENETIC GRAPH-BASED APPROACH FOR PARTITIONAL CLUSTERING." International Journal of Neural Systems 24, no. 03 (2014): 1430008. http://dx.doi.org/10.1142/s0129065714300083.

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Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric

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39

Punitha, S. C., K. Mugunthadevi, and M. Punithavalli. "Impact of Ontology based Approach on Document Clustering." International Journal of Computer Applications 22, no. 2 (2011): 22–26. http://dx.doi.org/10.5120/2556-3506.

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40

Williams, Philicity K., Caio V. Soares, and Juan E. Gilbert. "A Clustering Rule Based Approach for Classification Problems." International Journal of Data Warehousing and Mining 8, no. 1 (2012): 1–23. http://dx.doi.org/10.4018/jdwm.2012010101.

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Predictive models, such as rule based classifiers, often have difficulty with incomplete data (e.g., erroneous/missing values). So, this work presents a technique used to reduce the severity of the effects of missing data on the performance of rule base classifiers using divisive data clustering. The Clustering Rule based Approach (CRA) clusters the original training data and builds a separate rule based model on the cluster wise data. The individual models are combined into a larger model and evaluated against test data. The effects of the missing attribute information for ordered and unorder

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41

Keshari, Rita, and Amit Sinha. "An Intelligent Fuzzy Convex Hull based Clustering Approach." International Journal of Computer Applications 100, no. 8 (2014): 38–41. http://dx.doi.org/10.5120/17549-8145.

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42

Mourad, Mordjaoui, and Boudjema Bouzid. "Dynamic Hysteresis Model Based on Fuzzy Clustering Approach." Journal of Electrical Engineering and Technology 7, no. 6 (2012): 884–90. http://dx.doi.org/10.5370/jeet.2012.7.6.884.

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43

Najib, Fatma M., Rasha M. Ismail, Nagwa L. Badr, and Tarek F. Gharib. "Clustering based approach for incomplete data streams processing." Journal of Intelligent & Fuzzy Systems 38, no. 3 (2020): 3213–27. http://dx.doi.org/10.3233/jifs-191184.

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44

Kandwal, Rekha, Prerna Mahajan, and Ritu Vijay. "Rough Set Based Clustering Using Active Learning Approach." International Journal of Artificial Life Research 2, no. 4 (2011): 12–23. http://dx.doi.org/10.4018/jalr.2011100102.

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This paper revisits the problem of active learning and decision making when the cost of labeling incurs cost and unlabeled data is available in abundance. In many real world applications large amounts of data are available but the cost of correctly labeling it prohibits its use. In such cases, active learning can be employed. In this paper the authors propose rough set based clustering using active learning approach. The authors extend the basic notion of Hamming distance to propose a dissimilarity measure which helps in finding the approximations of clusters in the given data set. The underly

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45

Yong Wang, Lin Zuo, and Zhiguang Qin. "Clustering Online Crowd Interests: A Network-based Approach." International Journal of Digital Content Technology and its Applications 5, no. 5 (2011): 307–14. http://dx.doi.org/10.4156/jdcta.vol5.issue5.33.

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46

Saha, Moumita, and Pabitra Mitra. "Co-Clustering Based Approach for Indian Monsoon Prediction." Procedia Computer Science 51 (2015): 2938–42. http://dx.doi.org/10.1016/j.procs.2015.05.485.

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47

Wen, Dong, Lu Qin, Ying Zhang, Lijun Chang, and Xuemin Lin. "Efficient structural graph clustering: an index-based approach." VLDB Journal 28, no. 3 (2019): 377–99. http://dx.doi.org/10.1007/s00778-019-00541-4.

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48

Hadi, Raghad, Soukaena Hashem, and Abeer Maolood. "A Fuzzy Approach Based for Document Datasets Clustering." JOURNAL OF COLLEGE OF EDUCATION FOR PURE SCIENCE 9, no. 1 (2019): 1–13. http://dx.doi.org/10.32792/utq.jceps.09.01.01.

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49

Chatterjee, Sujoy, and Anirban Mukhopadhyay. "Clustering Ensemble: A Multiobjective Genetic Algorithm based Approach." Procedia Technology 10 (2013): 443–49. http://dx.doi.org/10.1016/j.protcy.2013.12.381.

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50

Chang, K. C., and M. F. Yeh. "Grey relational analysis based approach for data clustering." IEE Proceedings - Vision, Image, and Signal Processing 152, no. 2 (2005): 165. http://dx.doi.org/10.1049/ip-vis:20041209.

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