Minimally Infrequent Itemset Mining using Pattern-Growth Paradigm and Residual Trees

TL;DR

This paper introduces a novel pattern-growth algorithm utilizing residual trees for mining minimally infrequent itemsets, effectively handling multiple support thresholds and outperforming existing methods in efficiency.

Contribution

The paper presents a new algorithm for minimally infrequent itemset mining using residual trees and pattern-growth, enabling multi-level support thresholds and improved performance.

Findings

Outperforms existing infrequent itemset mining algorithms

Efficiently handles multiple support thresholds for different itemset lengths

Demonstrates effectiveness through comprehensive experiments

Abstract

Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Though most of the past work has been on finding frequent itemsets, infrequent itemset mining has demonstrated its utility in web mining, bioinformatics and other fields. In this paper, we propose a new algorithm based on the pattern-growth paradigm to find minimally infrequent itemsets. A minimally infrequent itemset has no subset which is also infrequent. We also introduce the novel concept of residual trees. We further utilize the residual trees to mine multiple level minimum support itemsets where different thresholds are used for finding frequent itemsets for different lengths of the itemset. Finally, we analyze the behavior of our algorithm with respect to different parameters and show through experiments that it outperforms the competing ones.