Financial market plays an important role in economy. Although funds developed only a few years in China, it has been a focal point in research and practice. The conventional methods analyzing fund data are fundamental analysis and technical analysis. Data mining can extract implicit, previously unknown and potentially useful knowledge from data. This paper presents the new technique to analyze ...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. This paper presents privacy preserving association rule mining across vertically partitioned data. We present an efficient algorithm to discover association rules with minimum levels of support and confidence, from heterogeneous data distributed across 2 parties, while preventing eit...

Sliding window is an interesting model to solve frequent pattern mining problem since it does not need entire history of received transactions and can handle concept change by considering recent data. However, in the previous sliding window algorithms, required amount of memory and processing time with respect to limited number of transactions within window is very large. To overcome this short...

Knowledge exploration from the large set of data, generated as a result of the various data processing activities due to data mining only. Frequent Pattern Mining is a very important undertaking in data mining. Apriori approach applied to generate frequent item set generally espouse candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how t...

Association rule mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rule mining model have been proposed so far; however, the problem of mining for mutually exclusive items has not been directly tackled yet. Such information could be useful in various cases (e.g., when the expression of a gen...

The general goal of data mining is to extract interesting correlated information from large collection of data. A key computationallyintensive subproblem of data mining involves nding frequent sets in order to help mine association rules for market basket analysis. Given a bag of sets and a probability, the frequent set problem is to determine which subsets occur in the bag with some minimum pr...

Intrusion detection systems (IDSs) have become a widely used measure for security, but we still have a problem on those systems results which includes many irrelevant alerts, so we will propose a data mining based method for classification to distinguish serious alerts and irrelevant one with the performance of 99.9 % in comparison with the other recent data mining methods which have reached th...

With the advancement of storage techniques and Digitization of work in every field, the amount of stored data is tremendously increasing. Influence in Information Technology has caused a sizeable change in every sector of the digitized world. One of such sectors is the stock market where data changes constantly. The economy of the country is indicative of the stock market; this sector needs mor...

Intrusion detection systems (IDSs) have become a widely used measure for security systems. The main problem for those systems results is the irrelevant alerts on those results. We will propose a data mining based method for classification to distinguish serious alerts and irrelevant one with a performance of 99.9 % which is better in comparison with the other recent data mining methods that hav...

Deductive databases may operate in a distributed environment. One powerful extension to a traditional deductive database is the incorporation of data mining and the use of association rules. This paper explores the issues that surround finding support and confidence for mining association rules.