With the ever-growing digital libraries and video databases, it is increasingly important to understand and mine the knowledge from video database automatically. Video association mining is a relatively new and emerging research trend used to discover and describe interesting patterns in video. The traditional classical association rule mining algorithms can not apply directly to the video. It ...

Missing values and incomplete data are a natural phenomenon in real datasets. If the association rules mine incomplete disregard of missing values, mistaken rules are derived. In association rule mining, treatments of missing values and incomplete data are important. This paper proposes novel technique to mine association rule from data with missing values from large voluminous databases. The p...

Data mining is the process of discovering significant and potentially useful knowledge in the form of patterns from the data. As a result, the notion of interestingness is very important for extracting useful knowledge patterns. Numerous interestingness measures have been discussed in the literature to assess the interestingness of a knowledge pattern. In this thesis, we focus on selecting a ri...

Extracting multilevel association rules in transaction databases is most commonly used tasks in data mining. This paper proposes a multilevel association rule mining using fuzzy concepts. This paper uses different fuzzy membership function to retrieve efficient association rules from multi level hierarchies that exist in a transaction dataset. In general, the data can spread into many hierarchi...

Mining association rules and mining sequential patterns both are to discover customer purchasing behaviors from a transaction database, such that the quality of business decision can be improved. However, the size of the transaction database can be very large. It is very time consuming to find all the association rules and sequential patterns from a large database, and users may be only interes...

Data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data stored in repositories, corporate databases, and data warehouses. In Data mining field, the primary task is to mine frequent item sets from a transaction database using Association Rule Mining (ARM).Whereas the extraction of frequent patterns has focused the majo...

Association rule mining has been an area of active research in the field of knowledge discovery and numerous algorithms have been developed to this end. Of late, data mining researchers have improved upon the quality of association rule mining for business development by incorporating the influential factors like value (utility), quantity of items sold (weight) and more, for the mining of assoc...

Market basket analysis is one of the typical applications in mining association rules. The valuable information discovered from data mining can be used to support decision making. Generally, support and confidence (objective) measures are used to evaluate the interestingness of association rules. However, in some cases, by using these two measures, the discovered rules may be not profitable and...

This paper investigates the mining of class association rules with the rough set approach. In data mining, an association occurs between the two sets of elements when one element set happen together with another. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. We present an efficient algorithm for mining the finest class rule se...

In this paper, the relation among factors in the road transportation sector from March, 2005 to March, 2011 is analyzed. Most of the previous studies have economical point of view on gasoline consumption. Here, a new approach is proposed in which different data mining techniques are used to extract meaningful relations between the aforementioned factors. The main and dependent factor is gasolin...