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LCTree-Based Approach for Mining Frequent Items in Real-Time.

Jiashun Chen1, Jianjing Chen2, Zhaoman Zhong1

  • 1School of Computer Engineering, Jiangsu Ocean University, Lianyungang, Jiangsu, China.

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A new Linking Compact Tree (LCTree) method efficiently discovers frequent itemsets in real-time data streams. LCTree reduces time and space complexity for stream data mining applications.

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Area of Science:

  • Computer Science
  • Data Mining
  • Algorithm Design

Background:

  • The increasing volume of real-time stream data necessitates efficient methods for knowledge discovery.
  • Discovering frequent itemsets in sliding windows requires optimized data structures for transaction storage and scanning.

Purpose of the Study:

  • To introduce a novel data structure, Linking Compact Tree (LCTree), for efficient frequent itemset mining in data streams.
  • To develop an algorithm with linear complexity for identifying frequent itemsets within sliding windows.

Main Methods:

  • Implemented an improved objective tree data structure for linear complexity frequent itemset discovery.
  • Utilized Head Linking List for efficient merging of items in sliding windows.
  • Employed Tail Linking List to facilitate the identification and removal of obsolete nodes.

Main Results:

  • LCTree achieves reduced time and space complexity for finding exact frequent items in data streams.
  • Experimental comparisons demonstrate superior performance of LCTree over established methods like Cantree, FP-tree, DSTree, CPSTree, and Gtree.

Conclusions:

  • LCTree is a promising solution for real-time frequent itemset detection.
  • The proposed method offers significant improvements in efficiency for stream data mining.