Building an associative classifier with multiple minimum supports

Abstract

Classification is one of the most important technologies used in data mining. Researchers have recently proposed several classification techniques based on the concept of association rules (also known as CBA-based methods). Experimental evaluations on these studies show that in average the CBA-based approaches can yield higher accuracy than some of conventional classification methods. However, conventional CBA-based methods adopt a single threshold of minimum support for all items, resulting in the rare item problem. In other words, the classification rules will only contain frequent items if minimum support (minsup) is set as high or any combinations of items are discovered as frequent if minsup is set as low. To solve this problem, this paper proposes a novel CBA-based method called MMSCBA, which considers the concept of multiple minimum supports (MMSs). Based on MMSs, different classification rules appear in the corresponding minsups. Several experiments were conducted with six real-world datasets selected from the UCI Machine Learning Repository. The results show that MMSCBA achieves higher accuracy than conventional CBA methods, especially when the dataset contains rare items.

Coverage-Based Classification Using Association Rule Mining

“…The step of finding frequent itemsets in associative classification is the most important and computationally expensive step [10,11]. Several different approaches to discover frequent rule items from a dataset have been adopted from association rule discovery.…”

“…AC aims to build accurate and efficient classifiers based on association rules. Researchers have proposed several classification algorithms based on association rules called associative classification methods, such as CBA: Classification-based Association [7], CMAR: Classification based on Multiple Association Rules [8], MCAR: Mining Class Association Rules [9], CPAR: Classification based on Predicted Association Rule [10], MMSCBA: Associative classifier with multiple minimum support [11], CBC: Associative classifier with a small number of rules [12], MMAC: Multi-class, multi-label associative classification [13], ARCID: Association Rule-based Classification for Imbalanced Datasets [14], and others.…”

“…Experimental evaluations [7,[10][11][12][13][14][15] show that associative classification approaches could achieve higher accuracy than some traditional classification algorithms produced by decision trees [16][17][18][19][20][21], rule induction [22][23][24][25][26] and non-rule-based approaches [27][28][29][30]. Most of the associative classification methods [8,9,[31][32][33][34][35][36][37] tend to build accurate and compact classifiers that include a limited number of effective rules by using rule selection or pruning techniques.…”

Distance based clustering of class association rules to build a compact, accurate and descriptive classifier

“…Associative Classification (AC) is a combination of these two important data mining techniques, namely, classification and association rule mining [3]. Recently, researchers have proposed several associative classification methods [4][5][6][7][8][9][10][11] that aim to build accurate and efficient classifiers based on association rules. Research studies prove that AC methods could achieve higher accuracy than some of the traditional classification methods, although the efficiency of AC methods depends on the user-defined parameters such as minimum support and confidence.…”

Mining Frequent Patterns of Crime using FP-Growth with Multiple Minimum Supports based on Shannon Entropy

“…FP-Growth has also been used intensively in crime patterns mining [5], [6], and [26]. Classical FP-Growth mines frequent patterns by using a single user-specified minimum support (abbreviated as minsup) [7]. However, using single minimum support for crime patterns mining is not adequate since it does not reflect the nature of each crime item in the dataset.…”