The vast amount of textual information available in electronic form is growing at a staggering rate in recent times. The task of mining useful or interesting frequent itemsets (words/terms) from very large text databases that are formed as a result of the increasing number of textual data still seems to be a quite challenging task. A great deal of attention in research community has been receiv...

Classical frequent itemset mining identifies frequent itemsets in transaction databases using only frequency of item occurrences, without considering utility of items. In many real world situations, utility of itemsets are based upon user’s perspective such as cost, profit or revenue and are of significant importance. Utility mining considers using utility factors in data mining tasks. Utility-...

Interesting patterns often occur at varied levels of support. The classic association mining based on a uniform minimum support, such as Apriori, either misses interesting patterns of low support or suuers from the bottleneck of itemset generation. A better solution is to exploit support constraints, which specify what minimum support is required for what itemsets, so that only necessary itemse...

We present a method for mining frequently occurring objects and scenes from videos. Object candidates are detected by finding recurring spatial arrangements of affine covariant regions. Our mining method is based on the class of frequent itemset mining algorithms, which have proven their efficiency in other domains, but have not been applied to video mining before. In this work we show how to e...

Oftenti mes we need to investigate m ore than one source of data to provide a solution to the proble m at hand. This data integration proble m has been investigated and largely solved for simple situations in traditional relational database m a n age me nt syste ms (RDBMSes). They typically provide a m e a ns for the user to join datasets together based on a co m mo n si mple attribute. Not all...

During the last ten years, many algorithms have been proposed to mine frequent itemsets. In order to fairly evaluate their behavior, the IEEE/ICDM Workshop on Frequent Itemset Mining Implementations (FIMI’03) has been recently organized. According to its analysis, kDCI++ is a state-of-the-art algorithm. However, it can be observed from the FIMI’03 experiments that its efficient behavior does no...

The organization, management and accessing of information in better manner in various data warehouse applications have been active areas of research for many researchers for more than last two decades. The work presented in this paper is motivated from their work and inspired to reduce complexity involved in data mining from data warehouse. A new algorithm named VS_Apriori is introduced as the ...

Image classification is one of the most useful and essential research field in computer vision domain and challenging task in the image management and retrieval system. The growing demands for image classification in computer vision having application such as video surveillance, image and video retrieval, web content analysis, biometrics etc. have pushed application developers to search and cla...

Data mining is a rapidly expanding field being applied in many disciplines, ranging from remote sensing to geographical information systems, computer cartography, environmental assessment and planning. Rule mining is a powerful technique used to discover interesting associations between attributes contained in a database (Han et al., 2006). Association rules can have one or several output attri...

Association rule mining (ARM) plays a vital role in data mining. It aims at searching for interesting pattern among items in a dense data set or database and discovers association rules among the large number of itemsets. The importance of ARM is increasing with the demand of finding frequent patterns from large data sources. Researchers developed a lot of algorithms and techniques for generati...