Tien Dung Do

Associative Classification With Artificial Immune System

Associative classification (AC), which is based on association rules, has shown great promise over many other classification techniques. To implement AC effectively, we need to tackle the problems on the very large search space of candidate rules during the rule discovery process and incorporate the discovered association rules into the classification process. This paper proposes a new approach that we call artificial immune system-associative classification (AIS-AC), which is based on AIS, for mining association rules effectively for classification. Instead of massively searching for all possible association rules, AIS-AC will only find a subset of association rules that are suitable for effective AC in an evolutionary manner. In this paper, we also evaluate the performance of the proposed AIS-AC approach for AC based on large datasets. The performance results have shown that the proposed approach is efficient in dealing with the problem on the complexity of the rule search space, and at the same time, good classification accuracy has been achieved. This is especially important for mining association rules from large datasets in which the search space of rules is huge.

Mining Frequent Itemsets with Category-Based Constraints

The discovery of frequent itemsets is a fundamental task of association rule mining. The challenge is the computational complexity of the itemset search space. One of the solutions for this is to use constraints to focus on some specific itemsets. In this paper, we propose a specific type of constraints called category-based as well as the associated algorithm for constrained rule mining based on Apriori. The Category-based Apriori algorithm reduces the computational complexity of the mining process by bypassing most of the subsets of the final itemsets. An experiment has been conducted to show the efficiency of the proposed technique.

Artificial immune system for associative classification

Artificial Immune Systems (AIS), which are inspired from nature immune system, have recently been investigated for many information processing applications, such as feature extraction, pattern recognition, machine learning and data mining. In this paper, we investigate AIS, and in particular the clonal selection algorithm for Associative Classification (AC). To implement associative classification effectively, we need to tackle the problems on the very large search space of candidate rules during the rule mining process. This paper proposes a new approach known as AIS-AC for mining association rules effectively for classification. In AIS-AC, we treat the rule mining process as an optimization problem of finding an optimal set of association rules according to some predefined constraints. The proposed AIS-AC approach is efficient in dealing with the complexity problem on the large search space of rules. It avoids searching greedily for all possible association rules, and is able to find an effective set of associative rules for classification.

Multiple-Step Rule Discovery for Associative Classification

Associative classification has shown great promise over many other classification techniques. However, one of the major problems of using association rule mining for associative classification is the very large search space of possible rules which usually leads to a very complex rule discovery process. This paper proposes a multiple-step rule discovery approach for associative classification called Mstep-AC. The proposed Mstep-AC approach focuses on discovering effective rules for data samples that might cause misclassification in order to enhance classification accuracy. Although the rule discovery process in Mstep-AC is performed multiple times to mine effective rules, its complexity is comparable with conventional associative classification approach. In this paper, we present the proposed Mstep-AC approach and its performance evaluation.

Mining association rules using relative confidence

Mining for association rules is one of the fundamental tasks of data mining. Association rule mining searches for interesting relationships amongst items for a given dataset based mainly on the support and confidence measures. Support is used for filtering out infrequent rules, while confidence measures the implication relationships from a set of items to one another. However, one of the main drawbacks of the confidence measure is that it presents the absolute value of implication that does not reflect truthfully the relationships amongst items. For example, if two items have a very high frequency, then they will probably form a rule with a high confidence even if there is no relationship between them at all. In this paper, we propose a new measure known as relative confidence for mining association rules, which is able to reflect truthfully the relationships of items. The effectiveness of the relative confidence measure is evaluated in comparison with the confidence measure in mining interesting relationships between terms from textual documents and in associative classification.