A Single Database Scan Approach for Mining Temporally Similar Association Patterns

Temporal pattern mining deals with discovering set of all temporal patterns of user interest from underlying input temporal database. The patterns which are of interest to user are to be discovered which requires scanning database repeatedly. The patterns which are not of interest to the user are called as outlier patterns. In this paper, we discover the temporal patterns which are of interest to the user by estimating support bound sequences of temporal patterns and then using these support sequences to estimate distance bounds with respect reference of interest. The distance measure chosen is Euclidean. Since Euclidean distance does not consist upper bound, we normalize the distance to make it feasible between bounds 0 and 1. The approach yields similar temporal patterns as that of conventional association patterns which requires multiple scans. In the best case, a single scan is sufficient however, in the worst; it is required to obtain true supports for some of the patterns. To estimate the support sequences we use the formal expressions designed to compute support bounds.

[1]  Vangipuram Radhakrishna,et al.  An Approach for Mining Similarity Profiled Temporal Association Patterns Using Gaussian Based Dissimilarity Measure , 2015 .

[2]  Shashi Shekhar,et al.  Similarity-Profiled Temporal Association Mining , 2009, IEEE Transactions on Knowledge and Data Engineering.

[3]  Stefan Conrad,et al.  Mining Several Kinds of Temporal Association Rules Enhanced by Tree Structures , 2010, 2010 Second International Conference on Information, Process, and Knowledge Management.

[4]  Charu C. Aggarwal,et al.  Outlier Detection for Temporal Data: A Survey , 2014, IEEE Transactions on Knowledge and Data Engineering.

[5]  Vangipuram Radhakrishna,et al.  A Survey on Temporal Databases and Data mining , 2015 .

[6]  Sushil Jajodia,et al.  Discovering calendar-based temporal association rules , 2003 .

[7]  John F. Roddick,et al.  ARMADA - An algorithm for discovering richer relative temporal association rules from interval-based data , 2007, Data Knowl. Eng..

[8]  Fei Wang,et al.  Frequence: interactive mining and visualization of temporal frequent event sequences , 2014, IUI.

[9]  Vangipuram Radhakrishna,et al.  A Novel Approach for Mining Similarity Profiled Temporal Association Patterns Using Venn Diagrams , 2015, ArXiv.

[10]  Ajith Abraham,et al.  An efficient algorithm for incremental mining of temporal association rules , 2010, Data Knowl. Eng..

[11]  Shashi Shekhar,et al.  Mining Temporal Association Patterns under a Similarity Constraint , 2008, SSDBM.

[12]  Charu C. Aggarwal,et al.  Outlier Detection for Temporal Data , 2014, Outlier Detection for Temporal Data.

[13]  Keshri Verma,et al.  Efficient calendar based temporal association rule , 2005, SGMD.

[14]  Keshri Verma,et al.  Temporal Approach to Association Rule Mining Using T-Tree and P-Tree , 2005, MLDM.

[15]  J. S. Yoo.  Temporal Data Mining: Similarity-Profiled Association Pattern , 2012 .

[16]  G. Maragatham,et al.  UTARM: an efficient algorithm for mining of utility-oriented temporal association rules , 2015, Int. J. Knowl. Eng. Data Min..

[17]  Tzung-Pei Hong,et al.  Mining hierarchical temporal association rules in a publication database , 2013, 2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing.

[18]  P. Krishna Reddy,et al.  An Efficient Approach to Mine Periodic-Frequent Patterns in Transactional Databases , 2011, PAKDD Workshops.

[19]  Yorick Wilks,et al.  Automatic Dating of Documents and Temporal Text Classification , 2006 .

[20]  Jiong Yang,et al.  TAR: temporal association rules on evolving numerical attributes , 2001, Proceedings 17th International Conference on Data Engineering.

[21]  Vangipuram Radhakrishna,et al.  An Efficient Approach to find Similar Temporal Association Patterns Performing Only Single Database Scan , 2016 .