Graph-based clustering for detecting frequent patterns in event log data

Finding frequent patterns is an important problem in data mining. We have devised a method for detecting frequent patterns in event log data. By representing events in a graph structure, we can generate clusters of frequently co-occurring events. This method is compared with basic association mining techniques and found to give a “macro-level” overview of patterns, which is more interpretable. In addition, the resulting graph-based clustering output for frequently co-occurring event sets is substantially less than association mining, while providing similar information levels. Therefore, the results are more manageable for practical applications.

[1]  Tai-Wen Yue,et al.  A Q'tron Neural-Network Approach to Solve the Graph Coloring Problems , 2007 .

[2]  Jian Pei,et al.  Mining Access Patterns Efficiently from Web Logs , 2000, PAKDD.

[3]  Yi Lu,et al.  Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree , 2005, Data Mining and Knowledge Discovery.

[4]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[5]  Jiawei Han,et al.  Frequent pattern mining: current status and future directions , 2007, Data Mining and Knowledge Discovery.

[6]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[7]  Risto Vaarandi,et al.  A Breadth-First Algorithm for Mining Frequent Patterns from Event Logs , 2004, INTELLCOMM.

[8]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[9]  Amedeo Napoli,et al.  Towards Rare Itemset Mining , 2007, 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007).

[10]  Nan Jiang,et al.  Research issues in data stream association rule mining , 2006, SGMD.

[11]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Jesús S. Aguilar-Ruiz,et al.  Gene association analysis: a survey of frequent pattern mining from gene expression data , 2010, Briefings Bioinform..

[13]  Takeaki Uno,et al.  Frequent Pattern Mining , 2016, Encyclopedia of Algorithms.

[14]  R. Sokal,et al.  THE COMPARISON OF DENDROGRAMS BY OBJECTIVE METHODS , 1962 .

[15]  M. Newman.  Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Edward Hung,et al.  Mining Frequent Itemsets from Uncertain Data , 2007, PAKDD.

[17]  Charu C. Aggarwal,et al.  Frequent Pattern Mining , 2014, Springer International Publishing.

[18]  Yun Sing Koh,et al.  Finding Sporadic Rules Using Apriori-Inverse , 2005, PAKDD.

[19]  Frans Coenen,et al.  Finding "interesting" trends in social networks using frequent pattern mining and self organizing maps , 2012, Knowl. Based Syst..

[20]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

[21]  Wynne Hsu,et al.  Mining association rules with multiple minimum supports , 1999, KDD '99.