Association Rule Mining is a machine learning method for discovering the interesting relations between attributes in huge transaction database. Typically, algorithms generate number of association rules, from which it hard to extract structured knowledge and present this automatically form that would be suitable user. Recently, an information cartography has been proposed creating summaries vis...

Classical frequent itemset mining identifies frequent itemsets in transaction databases using only frequency of item occurrences, without considering utility of items. In many real world situations, utility of itemsets are based upon user’s perspective such as cost, profit or revenue and are of significant importance. Utility mining considers using utility factors in data mining tasks. Utility-...

In previous work, we have described methods that we have developed for tuning a fuzzy data mining system for intrusion detection using a hierarchical genetic algorithm. Unfortunately, the genetic algorithm approach is very slow due to the computational cost of the evaluation function. In this paper, we describe parallel implementations of the genetic algorithm that were run on both a multiproce...

This paper describes how we applied a fuzzy technique to a data-mining task involving a large database that was provided by an international bank with offices in Hong Kong. The database contains the demographic data of over 320,000 customers and their banking transactions, which were collected over a six-month period. By mining the database, the bank would like to be able to discover interestin...