Association Rule is an important tool for today data mining technique. But this work only concern with positive rule generation till now. This paper gives study for generating negative and positive rule generation as demand of modern data mining techniques requirements. Here also gives detail of “A method for generating all positive and negative Association Rules” (PNAR). PNAR help to generates...

The extraction of useful information for decision making is a challenge in many different domains. Association rule mining is one of the most important techniques in this field, discovering relationships of interest among patterns. Despite the mining of association rules being an area of great interest for many researchers, the search for well-grouped continuous values is still a challenge, dis...

Mining association rules is to discover customer purchasing behaviors from a transaction database, such that the quality of business decision can be improved. However, the size of the transaction database can be very large. It is very time consuming to find all the association rules from a large database, and users may be only interested in some information. Moreover, the criteria of the discov...

Association rule mainly focuses on large transactional databases. In association rule mining all items are considered with equal weightage. But it is not suitable for all datasets. The weight should be considered based on the importance of the item. In our previous work HITS algorithm (Hyperlink Induced Topic Search) is used to find the weight of an item w-support is calculated for generating f...

Recently, there has been an increasing interest in applying association rule mining on data warehouses to identify trends and patterns that exist in the historical data present in large data warehouses. These warehouses have a complex underlying multidimensional structure and the application of traditional rule mining algorithms becomes hard. In this paper, we review and critically evaluate the...

Association Rule Mining is one of the most well liked techniques of data mining strategies whose primary aim is to extract associations among sets of items or products in transactional databases. However, mining association rules typically ends up in a really large amount of found rules, leaving the database analyst with the task to go through all the association rules and find out the interest...

Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces the number of rules to be presented. In this paper, we propose an eecient algorithm, CLOSET, for mi...

Data Mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binaryvalued transactions, however the data in real-world applications usually consists of quantitative values. In the last few years, many researchers have proposed Evolutionary Algorithms for mining interesting association rules from quantitative data. In this pape...

IDS (Intrusion Detection system) is an active and driving defense technology. This project mainly focuses on intrusion detection based on data mining. Data mining is to identify valid, novel, potentially useful, and ultimately understandable patterns in massive data. This project presents an approach to detect intrusion based on data mining frame work. Intrusion Detection System (IDS) is a popu...

Data mining is the process of extracting implicit, previously unknown, and potentially useful information from data in databases. It is widely recognized as a useful tool for decision making and knowledge discovery. Rule mining, however, is computationally expensive. Moreover, certain mathematical properties of mined rules have been given little attention. This paper applies logical identities ...