Abstract
The association rules represent an important class of knowledge that can be discovered from data warehouses. Current research efforts are focused on inventing efficient ways of discovering these rules from large databases. As databases grow, the discovered rules need to be verified and new rules need to be added to the knowledge base. Since mining afresh every time the database grows is inefficient, algorithms for incremental mining are being investigated. Their primary aim is to avoid or minimize scans of the older database b ....etc

[:=Read Full Message Here=:]

sir,
thanks for sharing KnowledgeBased Interactive Postmining of Association Rules Using Ontologies code ....etc

[:=Read Full Message Here=:]

to get information about the topic hiding sensitive association rules with limited side effects source code full report ppt and related topic refer the page link bellow

http://seminarsprojects.net/Thread-hiding-sensitive-association-rules-with-limited-side-effects

http://seminarsprojects.net/Thread-a-novel-algorithm-for-hiding-sensitive-frequent-itemsets?pid=7689&mode=threaded ....etc

[:=Read Full Message Here=:]

need a project in datamining:hiding sensitive association rule using clusters of sensitive association rule ....etc

[:=Read Full Message Here=:]

Report and ppt for validation of mining association rules with hierarchical clustering ....etc

[:=Read Full Message Here=:]

Abstract.
We present novel space and time-efficient algorithms for finding frequent items over general update streams. Our algorithms are based on a novel adaptation of the popular dyadic intervals method for finding frequent items. The algorithms improve upon existing algorithms in both theory and practice.
1 Introduction
There is a growing class of applications in areas of business and scientific data processing that continuously monitor large volumes of rapidly arriving data for detecting user-programmed scenarios, some of ....etc

[:=Read Full Message Here=:]

Hi I am doing some r&d on this. Please send me code for this base implementation atleast

thanks
Lakshman ....etc

[:=Read Full Message Here=:]

Hiding Sensitive Association Rules with Limited Side Effects

Abstract:
Data mining techniques have been widely used in various applications. However, the misuse of these techniques may lead to the disclosure of sensitive information. Researchers have recently made efforts at hiding sensitive association rules. Nevertheless, undesired side effects, e.g., non-sensitive rules falsely hidden and spurious rules falsely generated may be produced in the rule hiding process. In this paper, we present a nov ....etc

[:=Read Full Message Here=:]

Mining Weighted Association Rules without Preassigned Weights- IEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 20, NO. 4, JAVA
Abstract
Association rule mining is a key issue in data mining. However, the classical models ignore the difference between the transactions, and the weighted association rule mining does not work on databases with only binary attributes. In this paper, we introduce a new measure w-support, which does not require preassigned weights. It takes the quality of transactions into consideration using link ....etc

[:=Read Full Message Here=:]

Privacy Preserving Association Rules by Using Greedy Approach



Introduction
In the data mining, the most well-known method is the
Apriori algorithm. Since the Apriori algorithm is costly to find
candidate itemsets, there are many variants of Apriori that
differ in how they check candidate itemsets against the
database . For example, Zaki et al. presented the
algorithms MaxEclat and CHARM for identifying maximal
frequent itemsets. Furthermore, in employed an effic ....etc

[:=Read Full Message Here=:]