Highly Efficient Pattern Mining Based on Transaction Decomposition
- Resource Type
- Conference
- Authors
- Djenouri, Youcef; Chun-Wei Lin, Jerry; Norvag, Kjetil; Ramampiaro, Heri
- Source
- 2019 IEEE 35th International Conference on Data Engineering (ICDE) Data Engineering (ICDE), 2019 IEEE 35th International Conference on. :1646-1649 Apr, 2019
- Subject
- Computing and Processing
Approximation algorithms
Clustering algorithms
Data mining
Runtime
Itemsets
Clustering, Pattern Mining, Decomposition, Scalability.
- Language
- ISSN
- 2375-026X
This paper 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 transaction databases using clustering techniques. The set of transactions are first clus-tered using the k-means algorithm, where highly correlated transactions are grouped together. Next, the relevant patterns are derived by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one approximate and one exact. We demonstrate the efficiency and effectiveness of CBPM through a thorough experimental evaluation.