Association rule discovery is one of the primary tasks in data mining that extracts patterns to describe correlations between items in a transactional database. Using association rule mining for constructing classification systems is a promising approach. There are many associative classification approaches that have been proposed recently such as CBA, CMAR and MCAR. In this research paper, fou...

Association rule mining is one of the important problems of data mining. Single minimum support based approaches of association rule mining suffers from "rare item problem". An improved approach MSApriori uses multiple supports to generate association rules that consider rare item sets. Necessity to first identify the "large" set of items contained in the input dataset to ge...

Mining frequent itemsets over high speed, continuous and infinite data streams is a challenging problem due to changing nature of data and limited memory and processing capacities of computing systems. Sliding window is an interesting model to solve this problem since it does not need the entire history of received transactions and can handle concept change by considering only a limited range o...

The discovery of association rules is an important problem in data mining. It is a two-step process consisting of finding the frequent itemsets and generating association rules from them. Most of the research attention is focused on efficient methods of finding frequent itemsets because it is computationally the most expensive step. In this paper, we present a new data structure and a more effi...

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 ...

Discovering association rules is one of the important tasks in data mining. While most of the existing algorithms are developed for efficient mining of frequent patterns, it has been noted recently that some of the infrequent patterns, such as indirect associations, provide useful insight into the data. In this paper, we propose an efficient algorithm, called HI-mine, based on a new data struct...

The problem of incomplete data in the data mining is well known. In the literature many solutions to deal with missing values in various knowledge discovery tasks were presented and discussed. In the area of association rules the problem was presented mainly in the context of relational data. However, the methods proposed for incomplete relational database can not be easily adapted to incomplet...

Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic solutions. In the case of uncertain data, however, several new techniques have been proposed. Unfortunately, these proposals often suffer when a lot of items occur with many different probabilities. Here we propose an approach based on sampling by instantiating “possible worlds” of the uncertain d...

The proposed model helps mining frequent patterns in large databases by implementing queue data structure. The whole database is scanned only once and the data is compressed in the form of patterns in the queue structure. The frequent patterns are mined from this compressed database of queue structure. This queue approach brings efficiency in mining as the number of database scans is effectivel...