Efficient Maintenance and Mining of Frequent Itemsets over Online Data Streams with a Sliding Window

2006 ◽  
Author(s):  
Hua-Fu Li ◽  
Chin-Chuan Ho ◽  
Man-Kwan Shan ◽  
Suh-Yin Lee
Keyword(s):  
Data Streams ◽  
Sliding Window ◽  
Frequent Itemsets ◽  
Online Data

Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm

2012 ◽  
Vol 256-259 ◽  
pp. 2910-2913
Author(s):  
Jun Tan
Keyword(s):  
Data Streams ◽  
Sliding Window ◽  
Frequent Itemsets ◽  
Streaming Data ◽  
Efficient Data ◽  
Closed Itemsets ◽  
Frequent Itemsets Mining ◽  
Synthetic Datasets ◽  
Online Mining ◽  
Mining Data Streams

Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we proposed a novel sliding window based algorithm. The algorithm exploits lattice properties to limit the search to frequent close itemsets which share at least one item with the new transaction. Experiments results on synthetic datasets show that our proposed algorithm is both time and space efficient.

Finding frequent itemsets over online data streams

2006 ◽  
Vol 48 (7) ◽  
pp. 606-618 ◽  
Author(s):  
Joong Hyuk Chang ◽  
Won Suk Lee
Keyword(s):  
Data Streams ◽  
Frequent Itemsets ◽  
Online Data

scholarly journals Finding Frequent Itemsets over Data Streams with Sliding Window

2018 ◽  
Vol 11 (8) ◽  
pp. 85-94
Author(s):  
Jeong Hee Hwang ◽  
Hyeok Kim ◽  
Jeong Hee Chi
Keyword(s):  
Data Streams ◽  
Sliding Window ◽  
Frequent Itemsets

An Approximate Approach for Maintaining Recent Occurrences of Itemsets in a Sliding Window over Data Streams

2010 ◽  
pp. 308-327
Author(s):  
Jia-Ling Koh ◽  
Shu-Ning Shin ◽  
Yuan-Bin Don
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Traditional Approach ◽  
Experimental Studies ◽  
Dynamic Environment ◽  
Sliding Window ◽  
Fixed Time ◽  
Frequent Itemsets ◽  
Embedded Knowledge ◽  
Data Elements

Recently, the data stream, which is an unbounded sequence of data elements generated at a rapid rate, provides a dynamic environment for collecting data sources. It is likely that the embedded knowledge in a data stream will change quickly as time goes by. Therefore, catching the recent trend of data is an important issue when mining frequent itemsets over data streams. Although the sliding window model proposed a good solution for this problem, the appearing information of patterns within a sliding window has to be maintained completely in the traditional approach. For estimating the approximate supports of patterns within a sliding window, the frequency changing point (FCP) method is proposed for monitoring the recent occurrences of itemsets over a data stream. In addition to a basic design proposed under the assumption that exact one transaction arrives at each time point, the FCP method is extended for maintaining recent patterns over a data stream where a block of various numbers of transactions (including zero or more transactions) is inputted within a fixed time unit. Accordingly, the recently frequent itemsets or representative patterns are discovered from the maintained structure approximately. Experimental studies demonstrate that the proposed algorithms achieve high true positive rates and guarantees no false dismissal to the results yielded. A theoretic analysis is provided for the guarantee. In addition, the authors’ approach outperforms the previously proposed method in terms of reducing the run-time memory usage significantly.

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