Yudho Giri Sucahyo

Building a more accurate classifier based on strong frequent patterns

The classification problem in data mining is to discover models from training data for classifying unknown instances Associative classification builds the classifier rules using association rules and it is more accurate compared to previous methods In this paper, a new method named CSFP that builds a classifier from strong frequent patterns without the need to generate association rules is presented We address the rare item problem by using a partitioning method Rules generated are stored using a compact data structure named CP-Tree and a series of pruning methods are employed to discard weak frequent patterns Experimental results show that our classifier is more accurate than previous associative classification methods as well as other state-of-the-art non-associative classifiers.

TreeITL-Mine: Mining Frequent Itemsets Using Pattern Growth, Tid Intersection, and Prefix Tree

An important problem in data mining is the discovery of association rules that identify relationships among sets of items. Finding frequent itemsets is computationally the most expensive step in association rules mining, and so most of the research attention has been focused on it. In this paper, we present a more efficient algorithm for mining frequent itemsets. In designing our algorithm, we have combined the ideas of pattern-growth, tid-intersection and prefix trees, with significant modifications. We present performance comparisons of our algorithm against the fastest Apriori algorithm, and the recently developed H-Mine algorithm. We have tested all the algorithms using several widely used test datasets. The performance results indicate that our algorithm significantly reduces the processing time for mining frequent itemsets in dense data sets that contain relatively long patterns.

Efficiently mining frequent patterns from dense datasets using a cluster of computers

Efficient mining of frequent patterns from large databases has been an active area of research since it is the most expensive step in association rules mining. In this paper, we present an algorithm for finding complete frequent patterns from very large dense datasets in a cluster environment. The data needs to be distributed to the nodes of the cluster only once and the mining can be performed in parallel many times with different parameter settings for minimum support. The algorithm is based on a master-slave scheme where a coordinator controls the data parallel programs running on a number of nodes of the cluster. The parallel program was executed on a cluster of Alpha SMPs. The performance of the algorithm was studied on small and large dense datasets. We report the results of the experiments that show both speed up and scale up of our algorithm along with our conclusions and pointers for further work.

Improving the Efficiency of Frequent Pattern Mining by Compact Data Structure Design

Mining frequent patterns has been a topic of active research because it is computationally the most expensive step in association rule discovery. In this paper, we discuss the use of compact data structure design for improving the efficiency of frequent pattern mining. It is based on our work in developing efficient algorithms that outperform the best available frequent pattern algorithms on a number of typical data sets. We discuss improvements to the data structure design that has resulted in faster frequent pattern discovery. The performance of our algorithms is studied by comparing their running times on typical test data sets against the fastest Apriori, Eclat, FP-Growth and OpportuneProject algorithms. We discuss the performance results as well as the strengths and limitations of our algorithms.

Algebraic specification of association rule queries

In this paper, we present an algebraic specification for association rule queries that can form the foundation for integrating data mining and database management. We first define a set of nested algebraic operators needed to specify association rule queries. Association rule discovery is then expressed as a query tree of these operators. The expressiveness of the algebra is indicated by specifying some of the variants of association rule queries as query trees. Other variants of association rule queries discussed in the literature can also be represented using the algebra. Constrained association queries (CAQs) have been proposed by researchers to limit the number of rules discovered. We discuss the representation of CAQs using the algebra. Certain sequences of algebraic operators occur together in most of the query variants. These sequences are combined as modules to simplify the presentation of query trees. While the focus of the paper is the algebraic specification of association rule queries, we briefly discuss the optimization issues in implementing the algebra for association rule mining. The grouping of algebraic operators into modules facilitate the use of existing algorithms for association rules in query optimization.