Visual Exploration based Approach for Extracting the Interesting Association Rules (유용한 연관 규칙 추출을 위한 시각적 탐색 기반 접근법)

KIM JUNWOO (김준우); Hyun-Kyung Kang (강현경)

Visual Exploration based Approach for Extracting the Interesting Association Rules

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2013, 18(9), pp.177-187
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

KIM JUNWOO ¹, Hyun-Kyung Kang ²

¹동아대학교
²신라대학교

Accredited

ABSTRACT

Association rule mining is a popular data mining technique with a wide range of application domains, and aims to extract the cause-and-effect relations between the discrete items included in transaction data. However, analysts sometimes have trouble in interpreting and using the plethora of association rules extracted from a large amount of data. To address this problem, this paper aims to propose a novel approach called HTM for extracting the interesting association rules from given transaction data. The HTM approach consists of three main steps, hierarchical clustering,table-view, and mosaic plot, and each step provides the analysts with appropriate visual representation. For illustration, we applied our approach for analyzing the mass health examination data, and the result of this experiment reveals that the HTM approach help the analysts to find the interesting association rules in more effective way.

KEYWORDS

Association rule mining, Transaction data, Pre-processing, Visualization, Hierarchical clustering

Citation status

* References for papers published after 2023 are currently being built.

[confproc] R. Agrawal / 1993 / Mining Associations between Sets of Items in Massive Databases / Proceedings of the ACM-SIGMOD 1993 International Conference on Management of Data : 207~216

[confproc] R. Agrawal / 1994 / Fast Algorithms for Mining Association Rules / Proceedings of the International Conference on Very Large Databases : 125~131

[book] P.-N. Tan / 2005 / Introduction to Data Mining / Addison-Wesley

[confproc] A. Jorge / 2004 / Hierarchical Clustering for Thematic Browsing and Summarization of Large Sets of Association Rules / Proceedings of the 2004 SIAM International Conference on Data Mining

[journal] L. A. Fernandes / 2012 / Association Rule Visualization and Pruning through Response-Style Data Organization and Clustering / In Advances in Artificial Intelligence-IBERAMIA : 71~80

[journal] Y. A. Sekhavat / 2013 / Visualizing Association Rules Using Linked Matrix, Graph, and Detail Views / International Journal of Intelligence Science 3 : 34~49

[confproc] B. Schneiderman / 1996 / The Eyes Have It: A Task by Data Type Taxonomy for Information Visualization / Proceedings of the IEEE Symposium on Visual Languages : 336~343

[confproc] M. Klemettinen / 1994 / Finding Interesting Rules from Large Sets of Discovered Association Rules / Proceedings of the 3rd International Conference on Information and Knowledge Management : 401~407

[confproc] P. C. Wong / 1999 / Visualizing Association Rules for Text Mining / Proceedings of the 1999 IEEE Symposium on Information Visualization : 120~123

[journal] C. Romero / 2011 / RM-Tool: A Framework for Discovering and Evaluating Association Rules / Advances in Engineering Software 42(8) : 566~576

[confproc] R. J. Bayardo / 1999 / Mining the Most Interesting Rules / Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 145~154

[book] M. Hahsler / 2011 / Visualizing Association Rules: Introduction to the R-extension Package arulesViz / R project module

[book] K. Techapichetvanich / 2005 / VisAR: A New Technique for Visualizing Mined Association Rules, In Advanced Data Mining and Applications / Springer Berlin Heidelberg : 88~95

[confproc] Y. H. Fua / 1999 / Hierarchical Parallel Coordinates for Exploration of Large Datasets / Proceedings of the Conference on Visualization '99 : 43~50

[book] P. Buono / 2005 / Visualizing Association Rules in a Framework for Visual Data Mining, In Integrated Publication and Information Systems to Information and Knowledge Environments / Springer Berlin Heidelberg : 221~231

[journal] L. Yang / 2005 / Pruning and Visualizing Generalized Association Rules in Parallel Coordinates / IEEE Transactions on Knowledge and Data Engineering 17(1) : 60~70

[book] L. Yang / 2008 / Visual Exploration of Frequent Itemsets and Association Rules, In Visual Data Mining: Theory, Techniques and Tools for Visual Analytics / Springer Berlin Heidelberg : 60~75

[confproc] M. Hahsler / 2011 / Visualizing Association Rules in Hierarchical Groups / Proceedings of the 42nd Symposium on the Interface: Statistical, Machine Learning, and Visualization Algorithms

[confproc] K. H. Ong / 2002 / Crystalclear: Active Visualization of Association Rules / Proceedings of the ICDM-02 Workshop on Active Mining

[confproc] O. Couturier / 2007 / A Scalable Association Rule Visualization towards Displaying Large Amounts of Knowledge / Proceedings of 11th International Conference on Information Visualization IV 7 : 657~663

[journal] W. H. E. Day / 1984 / Efficient Algorithms for Agglomerative Hierarchical Clustering Method / Journal of Classification 1(1) : 7~24

[journal] A. Guenoche / 1991 / Efficient Algorithms for Divisive Hierarchical Clustering / Journal of Classification 8(1) : 5~30

[confproc] B. Sarwar / 2001 / Item-based Collaborative Filtering Recommendation Algorithms / Proceedings of the 10th International Conference on World Wide Web : 285~295

[confproc] C.-J. Tsui / 2010 / Building an IT Taxonomy with Co-occurrence Analysis, Hierarchical Clustering and Multidimensional Scaling / Proceedings of iConference : 247~256

[confproc] H. Hofmann / 2000 / Visualizing Association Rules with Interactive Mosaic Plots / Proceedings of the ACMKDD International Conference on Knowledge Discovery and Data Mining : 227~235

[confproc] A. Strehl / 1999 / Distance Based Clustering of Association Rules / Proceedings of ANNIE 1999 / ASME Press : 759~764

This paper was written with support from the National Research Foundation of Korea.

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65