Online Mining of Data Streams: Applications, Techniques and Progress

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.

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Online mining of temporal maximal utility itemsets from data streams

Proceedings of the 2010 ACM Symposium on Applied Computing - SAC '10 ◽

10.1145/1774088.1774436 ◽

2010 ◽

Cited By ~ 26

Author(s):

Bai-En Shie ◽

Vincent S. Tseng ◽

Philip S. Yu

Keyword(s):

Data Streams ◽

Online Mining

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Online mining of frequent sets in data streams with error guarantee

Knowledge and Information Systems ◽

10.1007/s10115-007-0106-2 ◽

2007 ◽

Vol 16 (2) ◽

pp. 245-258 ◽

Cited By ~ 16

Author(s):

Xuan Hong Dang ◽

Wee-Keong Ng ◽

Kok-Leong Ong

Keyword(s):

Data Streams ◽

Frequent Sets ◽

Online Mining

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XML Tree Classification on Evolving Data Streams

Advances in Data Mining and Database Management - XML Data Mining ◽

10.4018/978-1-61350-356-0.ch009 ◽

2011 ◽

pp. 199-218

Author(s):

Albert Bifet ◽

Ricard Gavaldà

Keyword(s):

Data Mining ◽

Data Streams ◽

General Framework ◽

Change Over Time ◽

The Core ◽

Advanced Analysis ◽

Classification Tasks ◽

Online Mining ◽

Core Issues ◽

Over Time

Nowadays, advanced analysis of data streams is quickly becoming a key area of data mining research, as the number of applications demanding such processing increases. Online mining when such data streams evolve over time, that is, when concepts drift or change completely, is becoming one of the core issues. At the same time, closure-based mining on relational data has recently provided some interesting algorithmic developments as well as practical uses. In this chapter we show how to use closure-based mining to reduce drastically the number of attributes in XML tree classification tasks. Moreover, using maximal frequent trees, we reduce even more the number of attributes needed in tree classification, in many cases without losing accuracy. We show a general framework to classify XML trees using subtree occurrence, composing a Tree XML Closed Frequent Miner with a classifier algorithm. We present specific methods that can adaptively mining closed patterns from data streams that change over time.

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Fast Mining of Closed Frequent Itemsets in Data Streams

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.231 ◽

2012 ◽

Vol 263-266 ◽

pp. 231-240

Author(s):

Yi Min Mao ◽

Zhi Gang Chen ◽

Li Xin Liu

Keyword(s):

Data Structure ◽

Data Streams ◽

High Speed ◽

Search Space ◽

Frequent Itemsets ◽

Streaming Data ◽

Closed Frequent Itemsets ◽

Item List ◽

Online Mining ◽

Mining Data Streams

With the emergence of large-volume and high-speed streaming data, traditional techniques for mining closed frequent itemsets has become inefficient. Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, a combinative data structure is designed by using an effective bit-victor to represent items and an extended dictionary frequent item list to record the current closed frequent information in streams. For tremendous reduction of search space, some new search strategies are proposed to avoid a large number of intermediate itemsets generated. Meanwhile, some new pruning strategies are also proposed for the purpose of efficiently and dynamically maintaining of all the closure check operations. Experimental results show that the method proposed is efficient in time, with sound scalability as the number of transactions processed increases and adapts rapidly to the changes in data streams.

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