Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target

In fuzzy classification of high-dimensional datasets, the number of fuzzy rules increases exponentially with the increase of attributes. Fuzzy association rule mining with appropriate threshold values can help to design a fuzzy classifier by significantly decreasing the number of interesting rules

-actionable attributes, the actions corresponding to a preferred class can be performed directly. However, standard association rule min-ing algorithms encounter many difficulties when applied to combined association rule mining, and hence new algorithms have to be developed for combined association rule mining

Abstract. This paper discusses the application of evolutionary multiobjective optimization (EMO) to association rule mining. We focus our attention especially on classification rule mining where the consequent part of each rule is a class label. First we briefly explain evolutionary multiobjective

Association rule algorithms typically only identify patterns that occur in the original form throughout the database. In databases which contain many small variations in the data, potentially important discoveries may be ignored as a result. In this paper, we describe an associate rule mining

Abstract We present a novel algorithm to compute large itemsets online. The user is free to change the support threshold any time during the rst scan of the transaction sequence. The algorithm maintains a superset of all large itemsets and for each itemset a shrinking, deterministic interval on its support. After at most 2 scans the algorithm terminates with the precise support for each large itemset. Typically our algorithm is by an order of magnitude more memory e cient than Apriori or DIC.

Abstract: Association Rule Mining (ARM) has been the area of interest for many researchers for a long time and continues to be the same. It is one of the important tasks of data mining. It aims at discovering relationships among various items in the database. The objective of this paper

, the association rule mining. We introduce new metrics in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining, and we show that the complexity of the new heuristics is similar to this of the original algorithms.

Abstract- One of the well-researched and most important techniques of mining data is Association Rule Mining. Association Rules as the name itself indicates includes finding correlations among sets of items in transaction database. Most famous algorithm of association rule mining is Apriori is used

We investigate ways to support interactive mining sessions, in the setting of association rule mining. In such sessions, users specify conditions (filters) on the associations to be generated. Our approach is a combination of the incorporation of filtering conditions inside the mining phase