An Efficient Algorithm for Maintaining Frequent Closed Itemsets over Data Stream

Authors:
Show-Jane Yen;Yue-Shi Lee;Cheng-Wei Wu;Chin-Lin Lin
Affiliations:
Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan County, Taiwan 333;Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan County, Taiwan 333;Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan County, Taiwan 333;Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan County, Taiwan 333
Venue:
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Year:
2009

Citing 7
Cited 3

Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Discovering Frequent Closed Itemsets for Association Rules

ICDT '99 Proceedings of the 7th International Conference on Database Theory
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CLOSET+: searching for the best strategies for mining frequent closed itemsets

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window

ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
CFI-Stream: mining closed frequent itemsets in data streams

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Compressed Hierarchical Mining of Frequent Closed Patterns from Dense Data Sets

IEEE Transactions on Knowledge and Data Engineering

Efficient algorithms for mining maximal high utility itemsets from data streams with different models

Expert Systems with Applications: An International Journal
Mining time-gap sequential patterns

IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Social circle analysis on ego-network based on context frequent pattern mining

Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service

Quantified Score

Hi-index	0.00

Visualization

Abstract

Data mining refers to the process of revealing unknown and potentially useful information from a large database. Frequent itemsets mining is one of the foundational problems in data mining, which is to discover the set of products that purchased frequently together by customers from a transaction database. However, there may be a large number of patterns generated from database, and many of them are redundant. Frequent closed itemset is a well-known condensed representation of frequent itemset, and it provides complete information of frequent itemsets. Extensive studies have been proposed for mining frequent closed itemsets from transaction database, but most of them do not take streaming data into consideration. In this paper, we propose an efficient algorithm for maintaining frequent closed itemsets over data streams. Whenever a transaction is added to database, our approach incrementally updates the information of closed itemsets and outputs updated frequent closed itemsets based on user-specified thresholds. The experimental results show that our approach outperforms previous studies.