In this paper we extend the state-of-art of the constraints that can be pushed in a frequent pattern computation. We introduce a new class of tough constraints, namely Loose Anti-monotone constraints, and we deeply characterize them by showing that they are a superclass of convertible anti-monotone constraints (e.g. constraints on average or median) and that they model tougher constraints (e.g....

• Candidates Generation and Test (Agrawal &Srikant, 1994; Heikki, Toivonen &Verkamo, 1994; Zaki et al., 1997): Starting at k=0, it first generates candidate k+1 itemsets from known frequent k itemsets and then counts the supports of the candidates to determine frequent k+1 itemsets that meet a minimum support requirement. • Sampling Technique (Toivonen, 1996): Uses a sampling method to select a...

Data mining refers to extracting knowledge from large amounts of data. Frequent itemsets is one of the emerging task in data mining. Frequent itemsets mining is crucial and most expensive step in association rule mining. The problem of mining frequent itemsets arises in large transactional databases where there is need to find association rules among the transactional data for the growth of bus...

Efficient algorithms to discover frequent patterns are essential in data mining research. Frequent pattern mining is emerging as powerful tool for many business applications such as e-commerce, recommender systems and supply chain management and group decision support systems to name a few. Several effective data structures, such as two-dimensional arrays, graphs, trees and tries have been prop...

Frequent pattern mining has been studied extensively. However, the effectiveness and efficiency of this mining is often limited, since the number of frequent patterns generated is often too large. In many applications it is sufficient to generate and examine only frequent patterns with support frequency in close-enough approximation instead of in full precision. Such a compact but close-enough ...

This paper describes an approach to identifying and comparing frequent pattern trends in social networks. A frequent pattern trend is defined as a sequence of time-stamped occurrence (support) values for specific frequent patterns that exist in the data. The trends are generated according to epochs. Therefore, trend changes across a sequence epochs can be identified. In many cases, a great many...

Frequent pattern mining (including the discovery of association rules) is an important task in data mining. Recently, there is increasing interest in mining relational databases. Up to now, most algorithms have focussed on a syntactical approach. However, the use of background knowledge would greatly improves the quality of the results. First, patterns and rules which are not equivalent from a ...

Discovery of frequent patterns is a very important data mining problem with numerous applications. Frequent pattern mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. A significant amount of research on efficient processing of frequent pattern queries has been done in recent years, focusing mainly on co...

In today’s emerging world, the role of data mining is increasing day by day with the new aspect of business. Data mining has been proved as a very basic tool in knowledge discovery and decision making process. Data mining technologies are very frequently used in a variety of applications. Frequent itemsets play an essential role in many data mining tasks that try to find interesting patterns fr...

Discovering clusters in subspaces, or subspace clustering and related clustering paradigms, is a research field where we find many frequent pattern mining related influences. In fact, as the first algorithms for subspace clustering were based on frequent pattern mining algorithms, it is fair to say that frequent pattern mining was at the cradle of subspace clustering—yet, it quickly developed i...