Data Mining can be defined as an activity that extracts some new nontrivial information contained in large databases. Traditional data mining techniques have focused largely on detecting the statistical correlations between the items that are more frequent in the transaction databases. Also termed as frequent itemset mining , these techniques were based on the rationale that itemsets which appe...

Frequent itemset mining is a popular and important first step in the analysis of data arising in a broad range of applications. The traditional “exact” model for frequent itemsets requires that every item occurs in each supporting transaction. Real data is typically subject to noise and measurement error. To date, the effects of noise on exact frequent pattern mining algorithms have been addres...

Data Mining can be delineated as an action that analyze the data and draws out some new nontrivial information from the large amount of databases. Traditional data mining methods have focused on finding the statistical correlations between the items that are frequently appearing in the database. High utility itemset mining is an area of research where utility based mining is a descriptive type ...

High-utility itemset mining is the task of finding the sets of items that yield a high utility (e.g. profit) in quantitative transaction databases. An important limitation of previous work on high-utility itemset mining is that utility is generally used as the sole criterion for assessing the interestingness of patterns. This leads to finding many itemsets that have a high profit but contain it...

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need differen...

Discovering maximal frequent itemset is a key issue in data mining; the Apriori-like algorithms use candidate itemsets generating/testing method, but this approach is highly time-consuming. To look for an algorithm that can avoid the generating of vast volume of candidate itemsets, nor the generating of frequent pattern tree, DCIP algorithm uses data-set condensing and intersection pruning to f...

We propose a novel approach for mining recent frequent itemsets. The approach has three key contributions. First, it is a single-scan algorithm which utilizes the special property of suffix-trees to guarantee that all frequent itemsets are mined. During the phase of itemset growth it is unnecessary to traverse the suffix-trees which are the data structure for storing the summary information of ...

Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Though most of the past work has been on finding frequent itemsets, infrequent itemset mining has demonstrated its utility in web mining, bioinformatics and other fields. In this paper, we propose a new algorithm based on the pattern-growth p...

A distributed algorithm based on Dynamic Itemset Counting (DIC) for generation of frequent itemsets is presented by us. DIC represents a paradigm shift from Apriori-based algorithms in the number of passes of the database hence reducing the total time taken to obtain the frequent itemsets. We exploit the advantage of Dynamic Itemset Counting in our algorithmthat of starting the counting of an i...

Technology is very influential in the world of increasingly fierce business competition so that people must find strategies to increase sales results midst competition. Ornamental plant sellers be smart managing stock and making selling ornamental plants. Transaction data can processed into information needed results, one which used as an analysis rules buyer transaction association purchasing ...