This paper applies the data mining techniques in the area of data cleaning as effective in discovering Constant Conditional Functional Dependencies(CCFDs) from relational databases . These CCFDs are used as business rules for context dependent data validations. Conditional Functional Dependencies(CFDs) are an extension of Functional dependencies(FDs) which captures the consistency of data by su...

Itemset mining is a data mining method extensively used for learning important correlations among data. Initially itemsets mining was made on discovering frequent itemsets. Frequent weighted item set characterizes data in which items may weight differently through frequent correlations in data’s. But, in some situations, for instance certain cost functions need to be minimized for determining r...

A data stream is a massive unbounded sequence of transactions continuously generated at a rapid rate, so how to process the transactions as fast as possible in the limited memory becomes an important problem. Although it has been studied extensively, most of the existing algorithms maintain a lot of infrequent itemsets, which causes huge space usage and inefficient update. In this paper, a new ...

In the area of knowledge discovery in databases, the generalized association rule mining is an extension from the traditional association rule mining by given a database and taxonomy over the items in database. More initiative and informative knowledge can be discovered. In this work, we propose a novel approach of generalized closed itemsets. A smaller set of generalized closed itemsets can be...

This paper presents new techniques for focusing the discovery of frequent itemsets within large, dense datasets containing highly frequent items. The existence of highly frequent items adds signi cantly to the cost of computing the complete set of frequent itemsets. Our approach allows for the exclusion of such items during the candidate generation phase of the Apriori algorithm. Afterwards, th...

Most algorithms for mining frequent patterns in data streams are based on structures like FP-tree, complex mining method makes time and storage space large compared to the bit vector expression. In this paper, an algorithm based on Horizontal Bit vectors for mining Frequent Patterns in data Streams HB-FPS is proposed. HB-FPS is divided into two phases, in online phase, it uses bit vectors to ho...

The itemsets discovered by traditional High Utility Itemsets Mining (HUIM) methods are more useful than frequent itemset mining outcomes; however, they are usually disordered and not actionable, and sometime accidental, because the utility is the only judgement and no relations among itemsets are considered. In this paper, we introduce the concept of combined mining to select combined itemsets ...

Finding frequent patterns from databases have been the most time consuming process in association rule mining. Several effective data structures, such as two-dimensional arrays, graphs, trees and tries have been proposed to collect candidate itemsets and frequent itemsets. It seems that the tree structure is most extractive to storing itemsets. The outstanding tree has been proposed so far is c...

The input of a classical application of association mining is a large set of transactions, each consisting of a list of items a customer has paid for at a supermarket checkout desk. The goal is to identify groups of items (“itemsets”) that frequently co-occur in the same shopping carts. This paper focuses on an aspect that has so far received relatively little attention: the composition of the ...

In this paper, we introduce an efficient algorithm using a new technique to find frequent itemsets from a huge set of itemsets called Cluster based Bit Vectors for Association Rule Mining (CBVAR). In this work, all the items in a transaction are converted into bits (0 or 1). A cluster is created by scanning the database only once. Then frequent 1-itemsets are extracted directly from the cluster...