Clustering Co-occurrence of Maximal Frequent Patterns in Streams

TL;DR

This paper introduces a clustering-based method to analyze the co-occurrence of maximal frequent patterns in data streams, helping to understand their structure despite the challenges of endless data and large pattern sets.

Contribution

It proposes a novel clustering approach for maximal frequent patterns in streams, enabling better structural analysis and pattern combination.

Findings

Clustering reveals the structure of data streams effectively.

Maximal frequent patterns are fewer and more informative.

The method improves understanding of pattern co-occurrence in streams.

Abstract

One way of getting a better view of data is using frequent patterns. In this paper frequent patterns are subsets that occur a minimal number of times in a stream of itemsets. However, the discovery of frequent patterns in streams has always been problematic. Because streams are potentially endless it is in principle impossible to say if a pattern is often occurring or not. Furthermore the number of patterns can be huge and a good overview of the structure of the stream is lost quickly. The proposed approach will use clustering to facilitate the analysis of the structure of the stream. A clustering on the co-occurrence of patterns will give the user an improved view on the structure of the stream. Some patterns might occur so much together that they should form a combined pattern. In this way the patterns in the clustering will be the largest frequent patterns: maximal frequent patterns. Our approach to decide if patterns occur often together will be based on a method of clustering when only the distance between pairs is known. The number of maximal frequent patterns is much smaller and combined with clustering methods these patterns provide a good view on the structure of the stream.