Let I = {i1, · · · , im} be a set of items. Let S be a stream of transactions in a sequential order, where each transaction is a subset of I. For an itemset X, which is a subset of I, a transaction T in S is said to contain the itemset X if X ⊆ T . The support of X is defined as the fraction of transactions in S that contain X. For a given support threshold s, X is frequent if the support of X ...

Association Rule Mining (ARM) has been the area of interest for many researchers for a long time and continues to be the same. It is one of the important tasks of data mining. It aims at discovering relationships among various items in the database. The objective of this paper is to present a review on the basic concepts of ARM technique along with the recent related work that has been done in ...

In the recent years, data mining has emerged as a very popular tool for extracting hidden knowledge from collection of large amount of data. One of the major challenges of data mining is to find the hidden knowledge in the data while the sensitive information is not revealed. Many strategies have been proposed to hide the information containing sensitive data. Privacy preserving data mining is ...

Problem of decision making, especially in financial issues is a crucial task in every business. Profit Pattern mining hit the target but this job is found very difficult when it is depends on the imprecise and vague environment, which is frequent in recent years. The concept of vague association rule is novel way to address this difficulty. Merely few researches have been carried out in associa...

Association Rule Mining (ARM) is one of the important data mining tasks that has been extensively researched by data-mining community and has found wide applications in industry. An Association Rule is a pattern that implies co-occurrence of events or items in a database. Knowledge of such relationships in a database can be employed in strategic decision making in both commercial and scientific...

The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery, that allow for the extraction of hidden knowledge in enormous...

Association Rule Mining is generally performed in Generation of frequent item sets & Rule generation. Mining association rules is not full of reward until it can be utilized to improve decision-making process of an organization. This paper is concerned with discovering positive and negative association rules. We present an Apriori-based algorithm that is able to find all valid positive and nega...

Association rule mining algorithm provides a means for determining rules and patterns from a large collection of data. However, when two sites want to engage in an association rule mining, data privacy concerns are raised. These concerns include loosing a competitive edge in the market place and breaching privacy laws. Techniques that have addressed this problem are data perturbation and homomo...

The association rule is used to find the items enumerated in the transactions. These rules are known as positive and negative rules. Many researchers have focused on positive association rule mining and developed many algorithms for finding the frequent item sets from the large databases however few have come up with the concept of negative association rule mining and therefore the lesser numbe...