Data mining is sorting through data to identify patterns and establish relationships. Association rule mining is a well established method of data mining that identifies significant correlations between items in transactional data. Measures like support count, comprehensibility and interestingness, used for evaluating a rule can be thought of as different objectives of association rule mining p...

Data mining methods including association rule mining and frequent episode mining have been applied to the intrusion detection problem. In other work, we have introduced modifications of these methods that mine fuzzy association rules and fuzzy frequent episodes and have described off-line methods that utilize these fuzzy methods for anomaly detection from audit data. In this paper we describe ...

Fuzzy set theory offers an important contribution to data mining leading to fuzzy data mining. It enables the management of interpretable and subjective information in both input and output of the data mining process. In this paper, we discuss the notion of interpretability in fuzzy data mining and we present some references on the management of emotions as a particular kind of subjective infor...

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-...

With the increment of users and information on the Web, mining processes inspired in the traditional data mining ones have been developed. This new recent area of investigation is called Web Mining. Within this area, we study the analysis of web log files in what is called Web Usage Mining. Different techniques of mining to discover usage patterns from web data can be applied in Web Usage Minin...

This report explores integrating fuzzy logic with two data mining methods (association rules and frequency episodes) for intrusion detection. Data mining methods are capable of extracting patterns automatically from a large amount of data. The integration with fuzzy logic can produce more abstract and flexible patterns for intrusion detection, since many quantitative features are involved in in...

Fuzzy utility mining reflects fuzzy degrees of quantities and profits for high utility itemsets. In generally, transaction time is also concerned, and not all products sold are always on the shelf. Thus, in this paper we present an effective framework, which considers the transaction period of each product from the first transaction it appears to the last transaction in the whole database for m...

Data mining is new but an interdisciplinary field utilizing statistics, machine learning, and other methods. In recent years, fuzzy logic has also been applied to augment data mining. The application of fuzzy logics makes the mining results more understandable and interpretable, apart from being useful and informative. Fuzzy rules are useful to summarize large databases. Several studies are don...

decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. the resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. the most comprehensible decision trees have been designed for perfect symbolic data. classical crisp decision trees (dt) are widely applied to classification t...

Mining patterns from fuzzy temporal data is an important data mining problem. One of these mining task is to find locally frequent sets, In most of the earlier works fuzziness was considered in the time attribute of the datasets .Although a couple of works have been done in dealing with such data, little has been done on the implementation side. In this article, we propose an efficient implemen...