Journal articles: 'Usage pattern mining'

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Patel, Ketul, and Dr A. R. Patel. "Process of Web Usage Mining to find Interesting Patterns from Web Usage Data." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (August 1, 2012): 144–48. http://dx.doi.org/10.24297/ijct.v3i1c.2767.

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The traffic on World Wide Web is increasing rapidly and huge amount of data is generated due to usersâ€™ numerous interactions with web sites. Web Usage Mining is the application of data mining techniques to discover the useful and interesting patterns from web usage data. It supports to know frequently accessed pages, predict user navigation, improve web site structure etc. In order to apply Web Usage Mining, various steps are performed. This paper discusses the process of Web Usage Mining consisting steps: Data Collection, Pre-processing, Pattern Discovery and Pattern Analysis. It has also presented Web Usage Mining applications and some Web Mining software.

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Handamari, Endang Wahyu. "Usage Pattern Exploration of Effective Contraception Tool." Journal of Research in Mathematics Trends and Technology 1, no. 1 (February 7, 2019): 1–6. http://dx.doi.org/10.32734/jormtt.v1i1.750.

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Determination of methods or contraception tool used by acceptors to support the Family Planning (“Keluarga Berencana”) is a problematic. In choosing methods or contraception tool, the acceptor must consider several factors, namely health factor, partner factor, and contraceptive method. Each method or contraception tool which is used has its advantages or disadvantages. Although it has been considering the advantages and disadvantages, it is still difficult to control fertility safely and effectively. Consequently acceptor change the method or a contraception tool that is used more than once. In order acceptors get the appropriate contraception tool then the patterns of changing in the usage of effective methods or contraception tool is determined. One of the methods that can be used to look for the patterns of changing in the usage of contraception tool is data mining. Data mining is an interesting pattern extraction of large amounts of data. A pattern is said to be interesting if the pattern is not trivial, implicit, previously unknown, and useful. The patterns presented should be easy to understand, can be applied to data that will be predicted with a certain degree, useful, and new. The early stage before applying data mining is using k nearest neighbors algorithm to determine the factors shortest distance selecting the contraception tool. The next step is applying data mining to usage changing data of method or contraception tool of family planning acceptors which is expected to dig up information related to acceptor behavior pattern in using the method or contraception tool. Furthermore, from the formed pattern, it can be used in decision making regarding the usage of effective contraception tool. The results obtained from this research is the k nearest neighbors by using the Euclidean distance can be used to determine the similarity of attributes owned by the acceptors of Family Planning to the training data is already available. Based on available training data, it can be determined the usage pattern of contraceptiion tool with the concept of data mining, where the acceptors of Family Planning are given a recommendation if the pattern is on the training data pattern. Conversely, if the pattern is none match, then the system does not provide recommendations of contraception tool which should be used.

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Et. al., V. Aruna,. "A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 1634–39. http://dx.doi.org/10.17762/turcomat.v12i2.1448.

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In the recent years with the advancement in technology, a lot of information is available in different formats and extracting the knowledge from that data has become a very difficult task. Due to the vast amount of information available on the web, users are finding it difficult to extract relevant information or create new knowledge using information available on the web. To solve this problem Web mining techniques are used to discover the interesting patterns from the hidden data .Web Usage Mining (WUM), which is one of the subset of Web Mining helps in extracting the hidden knowledge present in the Web log files , in recognizing various interests of web users and also in discovering customer behaviours. Web Usage mining includes different phases of data mining techniques called Data Pre-processing, Pattern Discovery & Pattern Analysis. This paper presents an updated focused survey on various sequential pattern mining algorithms like apriori-based algorithm , Breadth First Search-based strategy, Depth First Search strategy, sequential closed-pattern algorithm and Incremental pattern mining algorithm which are used in Pattern Discovery Phase of WUM. At last , a comparison is done based on the important key features present in these algorithms. This study gives us better understanding of the approaches of sequential pattern mining.

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Raman, Gokulapriya, and Ganesh Raj. "Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 244–56. http://dx.doi.org/10.22266/ijies2021.0228.24.

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Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user’s behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIPBSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIPBSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods.

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Yun, Unil, Gwangbum Pyun, and Eunchul Yoon. "Efficient Mining of Robust Closed Weighted Sequential Patterns Without Information Loss." International Journal on Artificial Intelligence Tools 24, no. 01 (February 2015): 1550007. http://dx.doi.org/10.1142/s0218213015500074.

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Sequential pattern mining has become one of the most important topics in data mining. It has broad applications such as analyzing customer purchase data, Web access patterns, network traffic data, DNA sequencing, and so on. Previous studies have concentrated on reducing redundant patterns among the sequential patterns, and on finding meaningful patterns from huge datasets. In sequential pattern mining, closed sequential pattern mining and weighted sequential pattern mining are the two main approaches to perform mining tasks. This is because closed sequential pattern mining finds representative sequential patterns which show exactly the same knowledge as the complete set of frequent sequential patterns, and weight-based sequential pattern mining discovers important sequential patterns by considering the importance of each sequential pattern. In this paper, we study the problem of mining robust closed weighted sequential patterns by integrating two paradigms from large sequence databases. We first show that the joining order between the weight constraints and the closure property in sequential pattern mining leads to different sets of results. From our analysis of joining orders, we suggest robust closed weighted sequential pattern mining without information loss, and present how to discover representative important sequential patterns without information loss. Through performance tests, we show that our approach gives high performance in terms of efficiency, effectiveness, memory usage, and scalability.

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Chen, Yu Ke, and Tai Xiang Zhao. "Association Rule Mining Based on Multidimensional Pattern Relations." Advanced Materials Research 918 (April 2014): 243–45. http://dx.doi.org/10.4028/www.scientific.net/amr.918.243.

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Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide adhoc, query driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis.

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Saied, Mohamed Aymen, Ali Ouni, Houari Sahraoui, Raula Gaikovina Kula, Katsuro Inoue, and David Lo. "Improving reusability of software libraries through usage pattern mining." Journal of Systems and Software 145 (November 2018): 164–79. http://dx.doi.org/10.1016/j.jss.2018.08.032.

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Krishna, J., and M. Haritha. "An Efficient Closed Maximal Pattern Sequences Mining on High Dimensional Datasets." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 50–53. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2088.

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Previous methods have presented convincing arguments that mining complete set of patterns is huge for effective usage. A compact but high quality set of patterns, such as closed patterns and maximal patterns is needed. Most of the previously maximal pattern sequences mining algorithms on high dimensional sequence, such as biological data set, work under the same support. In this paper, an efficient algorithm Closed Maximal Pattern Sequences (CMPS-Mine) for mining closed maximal patterns based on multi-support is suggested. Careful exhibitions once Beta-globin gene sequences have exhibited that CMPS-Mine expends less memory utilization and run time over Prefix Span. It generates compacted outcomes and two kinds of interesting patterns.

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PADMAKUMAR, SUJATHA, Dr PUNITHAVALLI Dr.PUNITHAVALLI, and Dr RANJITH Dr.RANJITH. "A Web Usage Mining Approach to User Navigation Pattern and Prediction in Web Log Data." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 92–94. http://dx.doi.org/10.15373/22778179/apr2014/34.

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Djenouri, Youcef, Jerry Chun-Wei Lin, Kjetil Nørvåg, Heri Ramampiaro, and Philip S. Yu. "Exploring Decomposition for Solving Pattern Mining Problems." ACM Transactions on Management Information Systems 12, no. 2 (June 2021): 1–36. http://dx.doi.org/10.1145/3439771.

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This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters. To boost the performance of the CBPM, a GPU-based implementation is investigated. To evaluate the CBPM framework, we perform extensive experiments on several pattern mining problems. The results from the experimental evaluation show that the CBPM provides a reduction in both the runtime and memory usage. Also, CBPM based on the approximate strategy provides good accuracy, demonstrating its effectiveness and feasibility. Our GPU implementation achieves significant speedup of up to 552× on a single GPU using big transaction databases.

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Hsu, Kuo-Wei. "Efficiently and Effectively Mining Time-Constrained Sequential Patterns of Smartphone Application Usage." Mobile Information Systems 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/3689309.

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Today, we have the freedom to install and use all kinds of applications on smartphones, thanks to the development of mobile communication and computing technologies. Undoubtedly, the system and application developers are eager to know how we use the applications on our smartphones in our daily life and so are the researchers. In this paper, we present our work on developing a pattern mining algorithm and applying it to smartphone application usage log collected from tens of smartphone users for several years. Our goal is to mine the sequential patterns each of which presents a series of application uses and satisfies a constraint on the maximum time interval between two application uses. However, we cannot mine such patterns by general algorithms and will miss some patterns by using the widely used implementation of the advanced algorithm specifically designed for time-constrained sequential pattern mining. We not only present an algorithm that can efficiently and effectively mine the patterns in which we are interested but also discuss and visualize the mined patterns. Our work could potentially support the related studies.

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Yun, Unil, and Eunchul Yoon. "An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 06 (December 2014): 879–912. http://dx.doi.org/10.1142/s0218488514500470.

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Based on the frequent pattern mining, closed frequent pattern mining and weighted frequent pattern mining have been studied to reduce the search space and discover important patterns. In the previous definition of weighted closed patterns, supports of patterns are only considered to compute the closures of the patterns. It means that the closures of weighted frequent patterns cannot be perfectly checked. Moreover, the usefulness of weighted closed frequent patterns depends on the presence of frequent patterns that have supersets with the exactly same weighted support. However, from the errors such as noise, slight changes in items' supports or weights by them have significantly negative effects on the mining results, which may prevent us from obtaining exact and valid analysis results since the errors can break the original characteristics of items and patterns. In this paper, to solve the above problems, we propose a concept of robust weighted closed frequent pattern mining, and an approximate bound is defined on the basis of the concept, which can relax requirements for precise equality among patterns' weighted supports. Thereafter, we propose a weighted approximate closed frequent pattern mining algorithm which not only considers the two approaches but also suggests fault tolerant pattern mining in the noise constraints. To efficiently mine weighted approximate closed frequent patterns, we suggest pruning and subset checking methods which reduce search space. We also report extensive performance study to demonstrate the effectiveness, efficiency, memory usage, scalability, and quality of patterns in our algorithm.

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Singh, Nanhay, Achin Jain, and Ram Shringar Raw. "Comparison Analysis of Web Usage Mining Using Pattern Recognition Techniques." International Journal of Data Mining & Knowledge Management Process 3, no. 4 (July 31, 2013): 137–47. http://dx.doi.org/10.5121/ijdkp.2013.3410.

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Ragupathy, G., and M. K. Prakash. "A Perspective Study on Pattern Discovery of Web Usage Mining." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 1076–81. http://dx.doi.org/10.26438/ijcse/v7i4.10761081.

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Liu, Binbin, Wei Dong, and Yinzhu Zhang. "Accelerating API-Based Program Synthesis via API Usage Pattern Mining." IEEE Access 7 (2019): 159162–76. http://dx.doi.org/10.1109/access.2019.2950232.

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Ramanathaiah, Ramakrishnan M., Bhawna Nigam, and M. Niranjanamurthy. "Construction of User’s Navigation Sessions from Web Logs for Web Usage Mining." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4432–37. http://dx.doi.org/10.1166/jctn.2020.9091.

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Web Usage Mining applies fewer techniques in record data to pull out the behavior of users. The knowledge mined from the web log can be utilized in web personalization, Prediction, prefetching, restructuring of web sites etc. It consists of three steps in preprocessing, pattern detection and analysis. Web log information is typically noisy and uncertain and preprocessing is a significant process ahead of mining. The Patterns discovered after applying the mining techniques are dependent on the accuracy of the weblog which in turn depends on the preprocessing phase. The output of preprocessing should be the user’s navigation session file. In this paper the techniques of preprocessing and the method for construction of user’s navigation session file is proposed.

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Edith Belise, Kenmogne, Nkambou Roger, Tadmon Calvin, and Engelbert Mephu Nguifo. "A heuristic to predict the optimal pattern-growth direction for the pattern growth-based sequential pattern mining approach." Journal of Advanced Computer Science & Technology 6, no. 2 (June 4, 2017): 20. http://dx.doi.org/10.14419/jacst.v6i2.7011.

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Sequential pattern mining is an efficient technique for discovering recurring structures or patterns from very large datasets, with a very large field of applications. It aims at extracting a set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan, PrefixSpan and their further extensions. The performances of these algorithms depend on how patterns grow. Because of this, we introduce a heuristic to predict the optimal pattern-growth direction, i.e. the pattern-growth direction leading to the best performance in terms of runtime and memory usage. Then, we perform a number of experimentations on both real-life and synthetic datasets to test the heuristic. The performance analysis of these experimentations show that the heuristic prediction is reliable in general.

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Nenadic, Goran, Irena Spasic, and Sophia Ananiadou. "Mining term similarities from corpora." Recent Trends in Computational Terminology 10, no. 1 (June 10, 2004): 55–80. http://dx.doi.org/10.1075/term.10.1.04nen.

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In this article, we present an approach to the automatic discovery of term similarities, which may serve as a basis for a number of term-oriented knowledge mining tasks. The method for term comparison combines internal (lexical similarity) and two types of external criteria (syntactic and contextual similarities). Lexical similarity is based on sharing lexical constituents (i.e. term heads and modifiers). Syntactic similarity relies on a set of specific lexico-syntactic co-occurrence patterns indicating the parallel usage of terms (e.g., within an enumeration or within a term coordination/conjunction structure), while contextual similarity is based on the usage of terms in similar contexts. Such contexts are automatically identified by a pattern mining approach, and a procedure is proposed to assess their domain-specific and terminological relevance. Although automatically collected, these patterns are domain dependent and identify contexts in which terms are used. Different types of similarities are combined into a hybrid similarity measure, which can be tuned for a specific domain by learning optimal weights for individual similarities. The suggested similarity measure has been tested in the domain of biomedicine, and some experiments are presented.

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Sudhamathy, G., and C. Jothi Venkateswaran. "An Efficient Hierarchical Frequent Pattern Analysis Approach for Web Usage Mining." International Journal of Computer Applications 43, no. 15 (April 30, 2012): 1–7. http://dx.doi.org/10.5120/6176-8603.

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Senkul, Pinar, and Suleyman Salin. "Improving pattern quality in web usage mining by using semantic information." Knowledge and Information Systems 30, no. 3 (February 24, 2011): 527–41. http://dx.doi.org/10.1007/s10115-011-0386-4.

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Madan Kumar, K. M. V., and B. Srinivasa Rao. "Mining Frequent Utility Sequential Patterns in Progressive Databases by U-Pisa." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 2020): 1786–95. http://dx.doi.org/10.1166/jctn.2020.8442.

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Sequential pattern mining is one of the most important aspects of data mining world and has a significant role in many applications like market analysis, biomedical analysis, weather forecasting etc. In the category of mining sequential patterns the usage of progressive database as an input database is relatively new and has a wide impact in decision-making system. In progressive sequential pattern mining, we discover the frequent sequences progressively with the help of period of Interest. As the traditional approaches of frequency based framework are not much more informative for decision making, in recent effort utility framework has been incorporated instead of frequency. This addressed many typical business concerns such as profit value associated with each pattern. In this paper, we applied the concept of frequent utility over the progressive database and discovered the sequential pattern efficiently. To do so we proposed an algorithm called U-Pisa which works progressively with the help of a quantitative progressive database. We conducted sub-stantial experiments on the proposed algorithm and proved that this process performs well.

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Mehta, Pradnya, Shailaja B. Jadhav, and R. B. Joshi. "Web Usage Mining for Discovery and Evaluation of Online Navigation Pattern Prediction." International Journal of Computer Applications 91, no. 4 (April 18, 2014): 23–26. http://dx.doi.org/10.5120/15870-4815.

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Srujan Kumarr, Kondi, M. Ashish Naidu, and Radha K. "Pattern Discovery with Web usage Mining using Apriori and FP-Growth Algorithms." International Journal of Computer Trends & Technology 67, no. 3 (March 25, 2019): 1–4. http://dx.doi.org/10.14445/22312803/ijctt-v67i3p101.

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Honarvar, Ali Reza, and Ashkan Sami. "Extracting Usage Patterns from Power Usage Data of Homes' Appliances in Smart Home using Big Data Platform." International Journal of Information Technology and Web Engineering 11, no. 2 (April 2016): 39–50. http://dx.doi.org/10.4018/ijitwe.2016040103.

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Advances in sensing techniques and IOT enabled the possibility to gain precise information about devices in smart home and smart city environments. Data analysis for sensors and devices may help us develop friendlier systems for smart city or smart home. Sequence pattern mining extracts interesting sequence pattern from data. Electricity usage dose follow a sequence of events. In this study the authors investigate this issue and extracted valuable sequence pattern from real appliances' power usage dataset using PrefixSpan. The experiments in this research is implemented on Spark as a novel distributed and parallel big data processing platform on two different clusters and interesting findings are obtained. These findings show the importance of extracting sequence pattern from power usage data to various applications such as decreasing CO2 and greenhouse gas emission by decreasing the electricity usage. The findings also show the needs to bring big data platforms to processing such kind of data which is captured in smart home and smart cities.

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Karthik, G. M., M. Sayeekumar, R. Kumaravel, and T. Aravind. "Finding spectrum occupancy pattern using CBFPP mining technique." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4361–68. http://dx.doi.org/10.3233/jifs-200368.

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The main challenge of problem lies in the perception of Cognitive Radio technology is to discover licensed empty spectrum pattern. The efficient model is needed for allocation among licensed and unlicensed users in wireless spectrum to improve the extraction rate and collision rate. To discover the spectrum hole in spectrum paging bands, stirred by FP mining technique proposed an efficient enumeration approach, namely Constraint Based Frequent Periodic Pattern Mining (CBFPP). The proposed algorithm uses TRIE-like data structure with data mining constraints. CBFPP algorithm predicts periodic spectrum occupancy holes in the paging bands. It is shown that CBFPP has a high prediction accuracy with reasonable time complexity. Experiment with synthetic and real data validate higher prediction accuracy and with reasonable time complexities. The unlicensed user utilizes the predicted spectrum pattern in spectrum usage of channel without significant interference to licensed users.

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M, Yori Apridonal, Febri Dristyan, and Afdhal Syafnur. "Aplikasi Web Usage Mining Menggunakan Metode Association Rule Dengan Algoritma Fp-Growth Untuk Mengetahui Pola Browsing Pengunjung Website." Prosiding Seminar Nasional Riset Information Science (SENARIS) 1 (September 30, 2019): 1060. http://dx.doi.org/10.30645/senaris.v1i0.117.

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As a way to improve the promotion of institutions via the web, there is a need for a method to view browsing patterns of visitors on the site unilak.ac.id, thereby showing the user's interest in the links he visits. Data mining or knowledge discovery is a process of extracting valuable information by analyzing the existence of certain patterns or relationships. To find visitor patterns in the form of association rules is to use the association rule method. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a set of data. FP-Growth is applied to get a pattern of visitors, about what links are frequently visited and seen by visitors on the site unilak.ac.id. This pattern is used to help web administrators in developing the site unilak.ac.id by utilizing knowledge from the association pattern to regulate the layout / layout design of the categories available on the site unilak.ac.id. From the results of processing the dataset with FP-Growth algorithm and processing data processed using data mining software, namely Rapidminer 6.5. It was found that the minimum value of support was 1% and the minimum confidence value of 50% resulted in 124 rules of association.

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Durojaiye, Ashimiyu B., Scott Levin, Matthew Toerper, Hadi Kharrazi, Harold P. Lehmann, and Ayse P. Gurses. "Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data." Journal of the American Medical Informatics Association 26, no. 6 (March 19, 2019): 506–15. http://dx.doi.org/10.1093/jamia/ocy184.

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Abstract Objectives The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes. Materials and Methods A process mining–based network analysis was applied to EHR metadata and trauma registry data for a cohort of pediatric trauma patients with minor injuries at a Level I pediatric trauma center. The EHR metadata were processed into an event log that was segmented based on gaps in the temporal continuity of events. A usage pattern was constructed for each encounter by creating edges among functional roles that were captured within the same event log segment. These patterns were classified into groups using graph kernel and unsupervised spectral clustering methods. Demographics, clinical and network characteristics, and emergency department (ED) length of stay (LOS) of the groups were compared. Results Three distinct usage patterns that differed by network density were discovered: fully connected (clique), partially connected, and disconnected (isolated). Compared with the fully connected pattern, encounters with the partially connected pattern had an adjusted median ED LOS that was significantly longer (242.6 [95% confidence interval, 236.9–246.0] minutes vs 295.2 [95% confidence, 289.2–297.8] minutes), more frequently seen among day shift and weekday arrivals, and involved otolaryngology, ophthalmology services, and child life specialists. Discussion The clique-like usage pattern was associated with decreased ED LOS for the study cohort, suggesting greater degree of collaboration resulted in shorter stay. Conclusions Further investigation to understand and address causal factors can lead to improvement in multidisciplinary collaboration.

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Hari Nallamala, Sri, Siva Kumar Pathuri, and Dr Suvarna Vani Koneru. "An Appraisal on Recurrent Pattern Analysis Algorithm from the Net Monitor Records." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 542. http://dx.doi.org/10.14419/ijet.v7i2.7.10879.

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Web data mining is a rising examination territory where taking out information is an essential job and a range of algorithms has been projected with a specific end goal to comprehend the an assortment of issues identified with web mining from available dataset. Here, we focus Frequent Pattern-Growth algorithm for data mining. Concerning FP-Growth, the efficiency is insufficient since mining progression is depends on large tree-frame data structure by internal memory estimate. We focuses on server monitor documents to find web convention forms of websites using web utilization mining and in exacting spotlights. Here, we had the practice to work with the projected strategy which could conceivable to eradicate the disadvantage of restriction of the presented rehearse in the area of web mining. An assortment of web usage mining practice can advance effort on numerous areas of scientific, medical & social media applications to advance toward for the research & security united zone. A briefed outline development system could help in gathering additional information on utilizing line up algorithm which shows the information state-plans effectually.

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., Neeru. "A SYSTEMATIC REVIEW ON DATA PREPROCESSING AND PATTERN DISCOVERY OF WEB USAGE MINING." International Journal of Advanced Research in Computer Science 9, no. 2 (April 20, 2018): 305–8. http://dx.doi.org/10.26483/ijarcs.v9i2.5763.

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Mohamad, Saad, and Abdelhamid Bouchachia. "Deep online hierarchical dynamic unsupervised learning for pattern mining from utility usage data." Neurocomputing 390 (May 2020): 359–73. http://dx.doi.org/10.1016/j.neucom.2019.08.093.

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G, Neelima, and Sireesha Rodda. "User Behavior Prediction Using Enhanced Pattern Tree Data Structure and Web Usage Mining." HELIX 9, no. 1 (February 28, 2019): 4732–37. http://dx.doi.org/10.29042/2019-4732-4737.

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Kannan, Alagesh. "Usage and Research Challenges in the Area of Frequent Pattern in Data Mining." IOSR Journal of Computer Engineering 13, no. 2 (2013): 08–13. http://dx.doi.org/10.9790/0661-1320813.

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Singh, Kuldeep, and Bhaskar Biswas. "Efficient Algorithm for Mining High Utility Pattern Considering Length Constraints." International Journal of Data Warehousing and Mining 15, no. 3 (July 2019): 1–27. http://dx.doi.org/10.4018/ijdwm.2019070101.

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High utility itemset (HUI) mining is one of the popular and important data mining tasks. Several studies have been carried out on this topic, which often discovers a very large number of itemsets and rules, which reduces not only the efficiency but also the effectiveness of HUI mining. In order to increase the efficiency and discover more interesting HUIs, constraint-based mining plays an important role. To address this issue, the authors propose an algorithm to discover HUIs with length constraints named EHIL (Efficient High utility Itemsets with Length constraints) to decrease the number of HUIs by removing tiny itemsets. EHIL adopts two new upper bound named sub-tree and local utility for pruning and modify them by incorporating length constraints. To reduce the dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. The execution time improvements ranged from a modest five percent to two orders of magnitude across benchmark datasets. The memory usage is up to twenty-eight times less than state-of-the-art algorithm FHM+.

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Rohdiniyah, Rahmi, Ibnu Asror, and Gede Agung Ary Wisudawan. "Penggunaan Metode berbasis Graph untuk Mining Frequent Sequential Access Pattern Pada Studi Kasus : Website iGracias Universitas Telkom." Indonesian Journal on Computing (Indo-JC) 2, no. 1 (September 14, 2017): 91. http://dx.doi.org/10.21108/indojc.2017.2.1.146.

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Penggunaan website pada bidang pendidikan, khususnya sebuah universitas, bertujuan untuk menyimpan berbagai informasi yang ada pada lingkungan universitas tersebut. Untuk itu, perlu dilakukan perbaikan struktur untuk memelihara kualitas dari web. Salah satu teknik yang dapat digunakan adalah dengan menggunakan web usage mining. Web usage mining merupakan salah satu cabang dari web mining yang digunakan untuk menemukan informasi atau pengetahuan yang bermanfaat dari pola navigasi user pada sebuah website. Pada penelitian ini menggunakan metode berbasis graph untuk frequent sequential access patterns dan menggunakan Igracias Universitas Telkom sebagai studi kasusnya. Karena Igracias selalu digunakan oleh seluruh entitas yang ada pada Universitas Telkom. Metode ini memiliki kelebihan untuk menemukan behavior pola pengaksesan user. Dari implementasi metoda ini didapat pola akses group user secara berurutan.

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Ridho, Farid, and Fachruddin Mansyur. "ANALISIS POLA PERMINTAAN PUBLIKASI DATA BADAN PUSAT STATISTIK MENGGUNAKAN ASSOCIATION RULE APRIORI." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 7, no. 2 (June 28, 2020): 187. http://dx.doi.org/10.20527/klik.v7i2.322.

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BPS is a data provider body in Indonesia. In publishing, BPS uses a variety of media, one of which is the BPS website. To get data through the BPS website, users can visit the website then download the data they need. The services obtained by data users on the BPS website depend on the quality of the website. The better the quality, the better the service experience gained by data users. The method that can be used to improve the quality of a website is the web usage mining method. Web usage mining is the application of data mining techniques on web repositories to study usage patterns. The purpose of this study is to determine the pattern of data publication requests on the BPS website which can later be used as a reference to improve the quality of BPS website services. Based on the results of the study, it was found that data users tend to access the same data with different years simultaneously. For results by grouping data by title without year, obtained quite diverse rules.Keywords: web usage mining, association rule, aprioriBPS merupakan badan penyedia data di Indonesia. Dalam mempublikasikan datanya, BPS menggunakan berbagai media, salah satunya adalah website BPS. Untuk mendapatkan data melalui website BPS, pengguna dapat mengunjungi website kemudian mengunduh data yang mereka butuhkan. Layanan yang didapatkan oleh pengguna data pada website BPS tergantung dari kualitas website tersebut. Semakin baik kualitasnya, semakin baik pula pengalaman pelayanan yang didapatkan oleh pengguna data. Metode yang dapat digunakan untuk meningkatkan kualitas suatu website adalah metode web usage mining. Web usage mining merupakan penerapan tekhnik data mining pada web repositori untuk mempelajari pola penggunaan. Tujuan dari penelitian ini adalah untuk mengetahui pola permintaan publikasi data pada website BPS yang nantinya dapat digunakan sebagai acuan untuk meningkatkan kualitas layanan website BPS. Berdasarkan hasil penelitian, didapatkan bahwa pengguna data cenderung mengakses data yang sama dengan tahun yang berbeda secara bersamaan. Untuk hasil dengan mengelompokan data berdasarkan judul tanpa tahun, diperoleh rules yang cukup beragam.Kata kunci: web usage mining, association rule, apriori

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Vellingiri, J., S. Kaliraj, S. Satheeshkumar, and T. Parthiban. "A Novel Approach for User Navigation Pattern Discovery and Analysis for Web Usage Mining." Journal of Computer Science 11, no. 2 (February 1, 2015): 372–82. http://dx.doi.org/10.3844/jcssp.2015.372.382.

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Lu, Eric Hsueh-Chan, and Ya-Wen Yang. "Mining mobile application usage pattern for demand prediction by considering spatial and temporal relations." GeoInformatica 22, no. 4 (April 26, 2018): 693–721. http://dx.doi.org/10.1007/s10707-018-0322-9.

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Kazeminuri, G., A. Harounabadi, and J. Mirabedini. "Web Personalization With Web Usage Mining Technics and Association Rules." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 1 (October 2, 2015): 6394–401. http://dx.doi.org/10.24297/ijct.v15i1.1711.

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As amount of information and web development increase considerably, some technics and methods are required to allow efficient access to data and information extraction from them. Extracting useful pattern from worldwide networks that are referred to as web mining is considered as one of the main applications of data mining. The key challenges of web users are exploring websites for finding the relevant information by taking minimum time in an efficient manner. Discovering the hidden knowledge in the manner of interaction in the web is considered as one of the most important technics in web utilization mining. Information overload is one of the main problems in current web and for tackling this problem the web personalization systems are presented that adapts the content and services of a website with user's interests and browsing behavior. Today website personalization is turned into a popular event for web users and it plays a leading role in speed of access and providing users' desirable information. The objective of current article is extracting index based on users' behavior and web personalization using web mining technics based on utilization and association rules. In proposed methods the weighting criteria showing the extent of interest of users to the pages are expressed and a method is presented based on combination of association rules and clustering by perceptron neural network for web personalization. The proposed method simulation results suggest the improvement of precision and coverage criteria with respect to other compared methods.

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Ding, Ming, Tianyu Wang, and Xudong Wang. "Establishing Smartphone User Behavior Model Based on Energy Consumption Data." ACM Transactions on Knowledge Discovery from Data 16, no. 2 (July 21, 2021): 1–40. http://dx.doi.org/10.1145/3461459.

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In smartphone data analysis, both energy consumption modeling and user behavior mining have been explored extensively, but the relationship between energy consumption and user behavior has been rarely studied. Such a relationship is explored over large-scale users in this article. Based on energy consumption data, where each users’ feature vector is represented by energy breakdown on hardware components of different apps, User Behavior Models (UBM) are established to capture user behavior patterns (i.e., app preference, usage time). The challenge lies in the high diversity of user behaviors (i.e., massive apps and usage ways), which leads to high dimension and dispersion of data. To overcome the challenge, three mechanisms are designed. First, to reduce the dimension, apps are ranked with the top ones identified as typical apps to represent all. Second, the dispersion is reduced by scaling each users’ feature vector with typical apps to unit ℓ 1 norm. The scaled vector becomes Usage Pattern, while the ℓ 1 norm of vector before scaling is treated as Usage Intensity. Third, the usage pattern is analyzed with a two-layer clustering approach to further reduce data dispersion. In the upper layer, each typical app is studied across its users with respect to hardware components to identify Typical Hardware Usage Patterns (THUP). In the lower layer, users are studied with respect to these THUPs to identify Typical App Usage Patterns (TAUP). The analytical results of these two layers are consolidated into Usage Pattern Models (UPM), and UBMs are finally established by a union of UPMs and Usage Intensity Distributions (UID). By carrying out experiments on energy consumption data from 18,308 distinct users over 10 days, 33 UBMs are extracted from training data. With the test data, it is proven that these UBMs cover 94% user behaviors and achieve up to 20% improvement in accuracy of energy representation, as compared with the baseline method, PCA. Besides, potential applications and implications of these UBMs are illustrated for smartphone manufacturers, app developers, network providers, and so on.

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Zhong, Jiang, Yi Feng Cheng, and Shi Tao Deng. "Web Recommendation Based on Unified Collaborative Filtering." Advanced Materials Research 219-220 (March 2011): 887–91. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.887.

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Web usage mining technique is widely used for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining can only discover usage pattern explicitly. In order to employ the users’ feature and web pages’ attributes to get more accuracy recommendation, we propose a unified collaborative filtering model for web recommendation which combined the latent and external features of users and web page through back propagation neural networks. In the algorithm, we employ Probabilistic Latent Semantic Analysis (PLSA) method to get latent features. The main advantages of this technique over standard memory-based methods are the higher accuracy, constant time prediction, and an explicit and compact model representation. The preliminary experimental evaluation shows that substantial improvements in accuracy over existing methods can be obtained.

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Goel, Archit, and Neha Verma. "Improve Enterprise Search using pattern matching and web mining techniques for E-Commerce Website." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 2 (December 28, 2013): 3261–67. http://dx.doi.org/10.24297/ijct.v12i2.3317.

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With the extensive expansion in the number of E-commerce websites, applying Web Usage Mining techniques to improve business is imperative. Also, employee as well as visitor satisfaction is important for an enterprise. This satisfaction is usually depends upon both, the effectiveness and efficiency of the search technology and how that information is published.

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Rathipriya, R., and K. Thangavel. "A Discrete Artificial Bees Colony Inspired Biclustering Algorithm." International Journal of Swarm Intelligence Research 3, no. 1 (January 2012): 30–42. http://dx.doi.org/10.4018/jsir.2012010102.

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Biclustering methods are the potential data mining technique that has been suggested to identify local patterns in the data. Biclustering algorithms are used for mining the web usage data which can determine a group of users which are correlated under a subset of pages of a web site. Recently, many blistering methods based on meta-heuristics have been proposed. Most use the Mean Squared Residue as merit function but interesting and relevant patterns such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of pattern since commonly the web users can present a similar behavior although their interest levels vary in different ranges or magnitudes. In this paper a new correlation based fitness function is designed to extract shifting and scaling browsing patterns. The proposed work uses a discrete version of Artificial Bee Colony optimization algorithm for biclustering of web usage data to produce optimal biclusters (i.e., highly correlated biclusters). It’s demonstrated on real dataset and its results show that proposed approach can find significant biclusters of high quality and has better convergence performance than Binary Particle Swarm Optimization (BPSO).

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Huynh, Bao, and Bay Vo. "An Efficient Method for Mining Erasable Itemsets Using Multicore Processor Platform." Complexity 2018 (October 22, 2018): 1–9. http://dx.doi.org/10.1155/2018/8487641.

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Mining erasable itemset (EI) is an attracting field in frequent pattern mining, a wide tool used in decision support systems, which was proposed to analyze and resolve economic problem. Many approaches have been proposed recently, but the complexity of the problem is high which leads to time-consuming and requires large system resources. Therefore, this study proposes an effective method for mining EIs based on multicore processors (pMEI) to improve the performance of system in aspect of execution time to achieve the better user experiences. This method also solves some limitations of parallel computing approaches in communication, data transfers, and synchronization. A dynamic mechanism is also used to resolve the load balancing issue among processors. We compared the execution time and memory usage of pMEI to other methods for mining EIs to prove the effectiveness of the proposed algorithm. The experiments show that pMEI is better than MEI in the execution time while the memory usage of both methods is the same.

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San-xing, Cao, Klein R. Rody, and Liu Jian-bo. "Enhancing the usage pattern mining performance with temporal segmentation of QPop increment in digital libraries." Journal of Zhejiang University-SCIENCE A 6, no. 11 (November 2005): 1290–96. http://dx.doi.org/10.1631/jzus.2005.a1290.

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Rathipriya, R., K. Thangavel, and J. Bagyamani. "Extraction of Target User Group from Web Usage Data Using Evolutionary Biclustering Approach." International Journal of Applied Metaheuristic Computing 2, no. 3 (July 2011): 69–79. http://dx.doi.org/10.4018/jamc.2011070104.

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Data mining extracts hidden information from a database that the user did not know existed. Biclustering is one of the data mining technique which helps marketing user to target marketing campaigns more accurately and to align campaigns more closely with the needs, wants, and attitudes of customers and prospects. The biclustering results can be tuned to find users’ browsing patterns relevant to current business problems. This paper presents a new application of biclustering to web usage data using a combination of heuristics and meta-heuristics algorithms. Two-way K-means clustering is used to generate the seeds from preprocessed web usage data, Greedy Heuristic is used iteratively to refine a set of seeds, which is fast but often yield local optimal solutions. In this paper, Genetic Algorithm is used as a global optimizer that can be coupled with greedy method to identify the global optimal target user groups based on their coherent browsing pattern. The performance of the proposed work is evaluated by conducting experiment on the msnbc, a clickstream dataset from UCI repository. Results show that the proposed work performs well in extracting optimal target users groups from the web usage data which can be used for focalized marketing campaigns.

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Kurniawati, Galuh Nurvinda, Rukun Santoso, and Sugito Sugito. "ANALISIS WEB USAGE MINING MENGGUNAKAN METODE MODIFIED GUSTAFSON – KESSEL CLUSTERING DAN ASSOCIATION RULE PADA WEBSITE UNIVERSITAS DIPONEGORO." Jurnal Gaussian 9, no. 4 (December 7, 2020): 486–94. http://dx.doi.org/10.14710/j.gauss.v9i4.29446.

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The comprehension of web visitors patterns are needed to develop website in an optimal fashion. The visitor pattern contained in the web log file of Diponegoro University’s website is clustered by Modified Gustafson-Kessel method. In general, this method produces two until six clusters. Two kinds of results are outlined in this paper. The first is the result contains two clusters, and the second is containing three clusters. In the first result, the visitors are divided into information seekers of student capacity and Engineering Faculty. In the second result, the visitors are divided into information seekers of Medicine Faculty, student admission and Engineering Faculty.

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Wu, Jimmy Ming-Tai, Qian Teng, Gautam Srivastava, Matin Pirouz, and Jerry Chun-Wei Lin. "The Efficient Mining of Skyline Patterns from a Volunteer Computing Network." ACM Transactions on Internet Technology 21, no. 4 (July 16, 2021): 1–20. http://dx.doi.org/10.1145/3423557.

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In the ever-growing world, the concepts of High-utility Itemset Mining (HUIM) as well as Frequent Itemset Mining (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these algorithms only used one factor to estimate an itemset. In the past, skyline pattern mining by considering both aspects of frequency and utility has been extensively discussed. In most cases, however, people tend to focus on purchase quantities of itemsets rather than frequencies. In this article, we propose a new knowledge called skyline quantity-utility pattern (SQUP) to provide better estimations in the decision-making process by considering quantity and utility together. Two algorithms, respectively, called SQU-Miner and SKYQUP are presented to efficiently mine the set of SQUPs. Moreover, the usage of volunteer computing is proposed to show the potential in real supermarket applications. Two new efficient utility-max structures are also mentioned for the reduction of the candidate itemsets, respectively, utilized in SQU-Miner and SKYQUP. These two new utility-max structures are used to store the upper-bound of utility for itemsets under the quantity constraint instead of frequency constraint, and the second proposed utility-max structure moreover applies a recursive updated process to further obtain strict upper-bound of utility. Our in-depth experimental results prove that SKYQUP has stronger performance when a comparison is made to SQU-Miner in terms of memory usage, runtime, and the number of candidates.

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Sorato, Danielly, Fábio B. Goularte, and Renato Fileto. "Short Semantic Patterns: A Linguistic Pattern Mining Approach for Content Analysis Applied to Hate Speech." International Journal on Artificial Intelligence Tools 29, no. 02 (March 2020): 2040002. http://dx.doi.org/10.1142/s0218213020400023.

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Microblog posts such as tweets frequently contain users’ opinions and thoughts about events, products, people, institutions, etc. However, the usage of social media to prop-agate hate speech is not an uncommon occurrence. Analyzing hateful speech in social media is essential for understanding, fighting and discouraging such actions. We believe that by extracting fragments of text that are semantically similar it is possible to depict recurrent linguistic patterns in certain kinds of discourse. Therefore, we aim to use these patterns to encapsulate frequent statements textually expressed in microblog posts. In this paper, we propose to exploit such linguistic patterns in the context of hate speech. Through a technique that we call SSP (Short Semantic Pattern) mining, we are able to extract sequences of words that share a similar meaning in their word embedding representation. By analyzing the extracted patterns, we reveal some kinds of discourses that are replayed across a dataset, such as racist and sexist statements. Afterwards, we experiment using SSP as features to build classifiers that detect if a tweet contains hate speech (binary classification) and to distinguish between sexist, racist and clean tweets (ternary classification). The SSP instances encountered in tweets containing sexism have shown that a large number of sexist tweets began with the introduction ‘I’m not sexist but’ and ‘Call me sexist but’. Meanwhile, SSP instances found in tweets reproducing racism revealed a prominence of contents against the Islamic religion, associated entities and organizations.

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Höpken, Wolfram, Marcel Müller, Matthias Fuchs, and Maria Lexhagen. "Flickr data for analysing tourists’ spatial behaviour and movement patterns." Journal of Hospitality and Tourism Technology 11, no. 1 (February 26, 2020): 69–82. http://dx.doi.org/10.1108/jhtt-08-2017-0059.

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Purpose The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios. Design/methodology/approach The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns. Findings The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent). Research limitations/implications As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour. Practical implications From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment. Originality/value The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

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Yin, Ming, Wenjie Wang, Yang Liu, and Dan Jiang. "An improvement of FP-Growth association rule mining algorithm based on adjacency table." MATEC Web of Conferences 189 (2018): 10012. http://dx.doi.org/10.1051/matecconf/201818910012.

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FP-Growth algorithm is an association rule mining algorithm based on frequent pattern tree (FP-Tree), which doesn’t need to generate a large number of candidate sets. However, constructing FP-Tree requires two scansof the original transaction database and the recursive mining of FP-Tree to generate frequent itemsets. In addition, the algorithm can’t work effectively when the dataset is dense. To solve the problems of large memory usage and low time-effectiveness of data mining in this algorithm, this paper proposes an improved algorithm based on adjacency table using a hash table to store adjacency table, which considerably saves the finding time. The experimental results show that the improved algorithm has good performance especially for mining frequent itemsets in dense data sets.

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Journal articles on the topic 'Usage pattern mining'

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