Mining Frequent Patterns in Data Stream over Sliding Windows

2009 ◽  
Author(s):  
Feng Wu ◽  
Quanyuan Wu ◽  
Yan Zhong ◽  
Xin Jin
Keyword(s):  
Data Stream ◽  
Frequent Patterns ◽  
Sliding Windows

Mining Frequent Patterns over Data Stream under the Time Decaying Model

2010 ◽  
Vol 36 (5) ◽  
pp. 674-684 ◽  
Author(s):  
Feng WU ◽  
Yan ZHONG ◽  
Quan-Yuan WU
Keyword(s):  
Data Stream ◽  
Frequent Patterns

Incremental mining of closed inter-transaction itemsets over data stream sliding windows

2011 ◽  
Vol 37 (2) ◽  
pp. 208-220 ◽  
Author(s):  
Shih-Chuan Chiu ◽  
Hua-Fu Li ◽  
Jiun-Long Huang ◽  
Hsin-Han You
Keyword(s):  
Data Stream ◽  
Incremental Mining ◽  
Sliding Windows

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.

Survey on Data Streams Clustering Techniques

2014 ◽  
Vol 933 ◽  
pp. 768-773 ◽  
Author(s):  
Wei Hua Ma
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Data Stream ◽  
Research Topic ◽  
Sliding Windows ◽  
Clustering Techniques ◽  
Research Results ◽  
Stream Clustering ◽  
Data Stream Clustering

Data stream in a popular research topic in big data era. There are many research results on data stream clustering domain. This paper firstly has a brief introduction to data stream methodologies, such as sampling, sliding windows, etc. Finally, it presents a survey on data streams clustering techniques.

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