Mining Frequent Itemsets from Sparse Data Streams in Limited Memory Environments

Web-Age Information Management - Lecture Notes in Computer Science ◽

10.1007/978-3-642-38562-9_5 ◽

2013 ◽

pp. 51-57 ◽

Cited By ~ 6

Author(s):

Juan J. Cameron ◽

Alfredo Cuzzocrea ◽

Fan Jiang ◽

Carson K. Leung

Keyword(s):

Data Streams ◽

Sparse Data ◽

Frequent Itemsets ◽

Limited Memory ◽

Mining Frequent Itemsets

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SuffixMiner: Efficiently Mining Frequent Itemsets in Data Streams by Suffix-Forest

Fuzzy Systems and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/11540007_72 ◽

2005 ◽

pp. 592-595

Author(s):

Lifeng Jia ◽

Chunguang Zhou ◽

Zhe Wang ◽

Xiujuan Xu

Keyword(s):

Data Streams ◽

Frequent Itemsets ◽

Mining Frequent Itemsets

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Mining frequent itemsets over data streams using efficient window sliding techniques

Expert Systems with Applications ◽

10.1016/j.eswa.2007.11.061 ◽

2009 ◽

Vol 36 (2) ◽

pp. 1466-1477 ◽

Cited By ~ 100

Author(s):

Hua-Fu Li ◽

Suh-Yin Lee

Keyword(s):

Data Streams ◽

Frequent Itemsets ◽

Mining Frequent Itemsets

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A survey on algorithms for mining frequent itemsets over data streams

Knowledge and Information Systems ◽

10.1007/s10115-007-0092-4 ◽

2007 ◽

Vol 16 (1) ◽

pp. 1-27 ◽

Cited By ~ 91

Author(s):

James Cheng ◽

Yiping Ke ◽

Wilfred Ng

Keyword(s):

Data Streams ◽

Frequent Itemsets ◽

Mining Frequent Itemsets

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Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis

Data Mining and Knowledge Discovery ◽

10.1007/s10618-010-0204-8 ◽

2010 ◽

Vol 23 (2) ◽

pp. 252-299 ◽

Cited By ~ 7

Author(s):

En Tzu Wang ◽

Arbee L. P. Chen

Keyword(s):

Data Streams ◽

Frequent Itemsets ◽

Distributed Data ◽

Distributed Data Streams ◽

Mining Frequent Itemsets

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Mining Frequent Itemsets in Large Data Warehouses: A Novel Approach Proposed for Sparse Data Sets

Intelligent Data Engineering and Automated Learning - IDEAL 2007 - Lecture Notes in Computer Science ◽

10.1007/978-3-540-77226-2_53 ◽

2007 ◽

pp. 517-526 ◽

Cited By ~ 1

Author(s):

S. M. Fakhrahmad ◽

M. Zolghadri Jahromi ◽

M. H. Sadreddini

Keyword(s):

Large Data ◽

Sparse Data ◽

Frequent Itemsets ◽

Data Sets ◽

Data Warehouses ◽

Novel Approach ◽

Sparse Data Sets ◽

Mining Frequent Itemsets

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A Survey on Mining Frequent Itemsets over Data Streams

International Journal of Computer Applications ◽

10.5120/ijca2017916030 ◽

2017 ◽

Vol 179 (8) ◽

pp. 37-40

Author(s):

Shailvi Maurya ◽

Sneha Ambhore ◽

Sneha Parit

Keyword(s):

Data Streams ◽

Frequent Itemsets ◽

Mining Frequent Itemsets

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Mining Frequent Itemsets (MFI) Over Data Streams: Variable Window Size (VWS) by Context Variation Analysis (CVA) of the Streaming Transactions

International Journal of Data Mining & Knowledge Management Process ◽

10.5121/ijdkp.2014.4402 ◽

2014 ◽

Vol 4 (4) ◽

pp. 17-25

Author(s):

Sidda Reddy V ◽

Rao T.V ◽

Govardhan A

Keyword(s):

Data Streams ◽

Window Size ◽

Frequent Itemsets ◽

Variation Analysis ◽

Variable Window ◽

Mining Frequent Itemsets

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Eficient Mining frequent itemsets with adaptive weights over data streams

Journal of Science Educational Science ◽

10.18173/2354-1075.2015-0062 ◽

2015 ◽

Vol 60 (7A) ◽

pp. 145-156

Author(s):

Nguyễn Hưng Long ◽

Nguyễn Thị Thu Thủy

Keyword(s):

Data Streams ◽

Frequent Itemsets ◽

Adaptive Weights ◽

Mining Frequent Itemsets

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A high-efficiency algorithm for Mining Frequent Itemsets over transaction data streams

2010 International Conference on Intelligent Control and Information Processing ◽

10.1109/icicip.2010.5565215 ◽

2010 ◽

Cited By ~ 1

Author(s):

Zhaoyang Qu ◽

Peng Li ◽

Yaying Li

Keyword(s):

Data Streams ◽

High Efficiency ◽

Frequent Itemsets ◽

Transaction Data ◽

Mining Frequent Itemsets

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Mining frequent itemsets in data streams using the weighted sliding window model

Expert Systems with Applications ◽

10.1016/j.eswa.2009.03.025 ◽

2009 ◽

Vol 36 (9) ◽

pp. 11617-11625 ◽

Cited By ~ 18

Author(s):

Pauray S.M. Tsai

Keyword(s):

Data Streams ◽

Sliding Window ◽

Frequent Itemsets ◽

Mining Frequent Itemsets

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