Association rules are the main technique to determine the frequent itemset in data mining. Apriori algorithm is a classical algorithm of association rule mining. This classical algorithm is inefficient due to so many scans of database. And if the database is large, it takes too much time to scan the database. In this paper, we proposed an Improved Apriori algorithm which reduces the scanning ti...

The Apriori algorithm is a popular correlation-based datamining kernel. However, it is a computationally expensive algorithm and the running times can stretch up to days for large databases, as database sizes can extend to Gigabytes. Through the use of a new extension to the systolic array architecture, time required for processing can be significantly reduced. Our array architecture implementa...

Data analysis is an important issue in business world in many respects. Different business organizations have data scientists, knowledge workers to analyze the business patterns and the customer behavior. Scrutinizing the past data to predict the future result has many aspects and understanding the nature of the query is one of them. Business analysts try to do this from a big data set which ma...

The organization, management and accessing of information in better manner in various data warehouse applications have been active areas of research for many researchers for more than last two decades. The work presented in this paper is motivated from their work and inspired to reduce complexity involved in data mining from data warehouse. A new algorithm named VS_Apriori is introduced as the ...

One problem in query reformulation process is to nd an optimal set of terms to add to the old query. In our TREC experiments this year, we propose to use the association rule discovery (especially apriori algorithm) to nd good candidate terms to enhance the query. These candidate terms are automatically derived from collection, added to the original query to build a new one. Experiments conduct...

Implementations of the well-known Apriori algorithm for finding frequent item sets and associations rules usually rely on a doubly recursive scheme to count the subsets of a given transaction. This process can be accelerated if the recursion is restricted to those parts of the tree structure that hold the item set counters whose values are to be determined in the current pass (i.e., contain a p...

We define sporadic rules as those with low support but high confidence: for example, a rare association of two symptoms indicating a rare disease. To find such rules using the well-known Apriori algorithm, minimum support has to be set very low, producing a large number of trivial frequent itemsets. We propose “Apriori-Inverse”, a method of discovering sporadic rules by ignoring all candidate i...

The issue of educational evaluation has long been a research hotspot. Using big data analysis method to conduct educational evaluation can improve the pertinence and effectiveness of education. Conventional Apriori algorithm has certain limitations in the application of educational evaluation. This paper introduces an improved Apriori-Gen algorithm and describes its application in evaluation of...

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...