The Thesis aims at in depth study of Graph based approach to find Association Rule Mining and Association Rule Mining without pre-assign weights and its implementation. In the past, several authors have proposed various association rule mining algorithms with and without pre-assigned weights. The Thesis consists of two parts. Part two consists of graph based association rule mining for primitiv...

The increasing bulk of data generation in industrial and scientific applications has fostered practitioners’ interest in mining large amounts of unlabeled data in the form of continuous, high speed, and time-changing streams of information. An appealing field is association stream mining, which models dynamically complex domains via rules without assuming any a priori structure. Different from ...

This report explores integrating fuzzy logic with two data mining methods (association rules and frequency episodes) for intrusion detection. Data mining methods are capable of extracting patterns automatically from a large amount of data. The integration with fuzzy logic can produce more abstract and flexible patterns for intrusion detection, since many quantitative features are involved in in...

This project [4] centers on regional association rule mining and scoping in spatial datasets. We introduces a methodology for mining spatial association rules and proposes new algorithms to determine the scope of a spatial association rule. We develop a reward-based region discovery framework that employs clustering to find interesting regions. The framework is applied to solve two distinct reg...

Association rule mining is the process of finding some relations among the attributes/attribute values of huge database based on support value. Most existing association mining techniques are developed to generate frequent rules based on frequent itemsets generated on market basket datasets. A common property of these techniques is that they extract frequent itemsets and prune the infrequent it...

Association Rule Mining (ARM) is concerned with how items in a transactional database are grouped together. It is commonly known as market basket analysis, because it can be likened to the analysis of items that are frequently put together in a basket by shoppers in a market. From a statistical point of view, it is a semiautomatic technique to discover correlations among a set of variables. ARM...

Traditional association rule mining method mines association rules only for the items bought by the customer. However an actual transaction consists of the items bought by the customer along with the quantity of items bought. This paper reconsiders the traditional database by taking into account both items as well as its quantity. This new transaction database is named as bag database and each ...

The problem of mining association rules for fuzzy quantitative items was introduced and an algorithm proposed in [5]. However, the algorithm assumes that fuzzy sets are given. In this paper we propose a method to find the fuzzy sets for each quantitative attribute in a database by using clustering techniques. We present a scheme for finding the optimal partitioning of a data set during the clus...

Association Rule Mining (ARM) is concerned with how items in a transactional database are grouped together. It is commonly known as market basket analysis, because it can be likened to the analysis of items that are frequently put together in a basket by shoppers in a market. From a statistical point of view, it is a semiautomatic technique to discover correlations among a set of variables. ARM...

Data mining is a field encompassing study of the tools and techniques to assist humans in intelligently analyzing (mining) mountains of data. Data mining has found successful applications in many fields including sales and marketing, financial crime identification, portfolio management, medical diagnosis, manufacturing process management and health care improvement etc.. Data mining techniques ...