Most of the mining techniques have only concerned with interesting patterns. However, in the recent years, there is an increasing demand in mining Unexpected Items or Outliers or Rare Items. Several application domains have realized the direct mapping between outliers in data and real world anomalies that are of great interest to an analyst. Outliers represents semantically correct but infreque...

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM-Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (...

The discovery of unexpected behaviors in databases is an interesting problem for many real-world applications. In previous studies, unexpected behaviors are primarily addressed within the context of patterns, association rules, or sequences. In this paper, we study the unexpectedness with respect to the fuzzy recurrence behaviors contained in sequence databases. We first propose the notion of f...

This paper shows how a small number of fuzzy rules can be selected for designing interpretable fuzzy rule-based classification systems. Our approach consists of two phases: candidate rule generation by data mining criteria and rule selection by genetic algorithms. First a large number of candidate rules are generated and prescreened using two rule evaluation criteria in data mining. Next a smal...

This paper is concerned with proposing the fuzzy cognitive map (FCM)-driven inference ampli®cation mechanism in the ®eld of web-mining. As the recent advent of the Internet, most of the modern ®rms are now geared towards using the web technology in their daily as well as strategic activities. The web-mining technology provides them with unprecedented ability to analyze web-log data, which are s...

The integration of fuzzy logic with data mining methods such as association rules has achieved interesting results in various digital forensics applications. As a data mining technique, the association rule mining (ARM) algorithm uses ranges to convert any quantitative features into categorical ones. Such features lead to the sudden boundary problem, which can be smoothed by incorporating fuzzy...

In this study, we introduce a method for defect classification using relational association rule mining based on fuzzy classifier along with modified artificial bee colony algorithm. Relational association rules are an extension of ordinal association rules, which are a particular type of association rules that describe numerical orderings between attributes that commonly occur in the data. The...