Sliding window based weighted maximal frequent pattern mining over data streams

2014 ◽  
Vol 41 (2) ◽  
pp. 694-708 ◽  
Author(s):  
Gangin Lee ◽  
Unil Yun ◽  
Keun Ho Ryu
Keyword(s):  
Data Streams ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Sliding Window ◽  
Frequent Pattern ◽  
Maximal Frequent Pattern

Sliding window-based frequent pattern mining over data streams

2009 ◽  
Vol 179 (22) ◽  
pp. 3843-3865 ◽  
Author(s):  
Syed Khairuzzaman Tanbeer ◽  
Chowdhury Farhan Ahmed ◽  
Byeong-Soo Jeong ◽  
Young-Koo Lee
Keyword(s):  
Data Streams ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Sliding Window ◽  
Frequent Pattern

scholarly journals An Efficient Algorithm for Sliding Window-Based Weighted Frequent Pattern Mining over Data Streams

2009 ◽  
Vol E92-D (7) ◽  
pp. 1369-1381 ◽  
Author(s):  
Chowdhury Farhan AHMED ◽  
Syed Khairuzzaman TANBEER ◽  
Byeong-Soo JEONG ◽  
Young-Koo LEE
Keyword(s):  
Data Streams ◽  
Efficient Algorithm ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Sliding Window ◽  
Frequent Pattern

An Efficient Frequent Patterns Mining Algorithm over Data Streams Based on FPD-Graph

2012 ◽  
Vol 433-440 ◽  
pp. 4457-4462 ◽  
Author(s):  
Jun Shan Tan ◽  
Zhu Fang Kuang ◽  
Guo Gui Yang
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Data Generation ◽  
Experiment Data ◽  
Mining Algorithm ◽  
Head Node

The design of synopses structure is an important issue of frequent patterns mining over data stream. A data stream synopses structure FPD-Graph which is based on directed graph is proposed in this paper. The FPD-Graph contains list head node FPDG-Head and list node FPDG-Node. The operations of FPD-Graph consist of insert operation and deletion operation. A frequent pattern mining algorithm DGFPM based on sliding window over data stream is proposed in this paper. The IBM synthesizes data generation which output customers shopping a data are adopted as experiment data. The DGFPM algorithm not only has high precision for mining frequent patterns, but also has low processing time.

Diversifying Search Results through Pattern-Based Subtopic Modeling

2012 ◽  
Vol 8 (4) ◽  
pp. 37-56 ◽  
Author(s):  
Wei Zheng ◽  
Hui Fang ◽  
Hong Cheng ◽  
Xuanhui Wang
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Context Information ◽  
Redundant Information ◽  
Clustering Method ◽  
Retrieval Models ◽  
Search Results ◽  
Maximal Frequent Pattern ◽  
Single Pattern

Traditional information retrieval models do not necessarily provide users with optimal search experience because the top ranked documents may contain excessively redundant information. Therefore, satisfying search results should be not only relevant to the query but also diversified to cover different subtopics of the query. In this paper, the authors propose a novel pattern-based framework to diversify search results, where each pattern is a set of semantically related terms covering the same subtopic. They first apply a maximal frequent pattern mining algorithm to extract the patterns from retrieval results of the query. The authors then propose to model a subtopic with either a single pattern or a group of similar patterns. A profile-based clustering method is adapted to group similar patterns based on their context information. The search results are then diversified using the extracted subtopics. Experimental results show that the proposed pattern-based methods are effective to diversify the search results.

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