Data mining is an area of data analysis that has arisen in response to new data analysis challenges, such as those posed by massive data sets or non-traditional types of data. Association analysis, which seeks to find patterns that describe the relationships of attributes (variables) in a binary data set, is an area of data mining that has created a unique set of data analysis tools and concept...

Many existing distributed data mining algorithms do not allow users to express the patterns to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. Moreover, due to inherited measurement inaccuracies and/or network latencies, data are often riddled with uncertainty. Th...

This paper presents an extension of GUHA method for relational data mining of association rules. Because ILP methods are well established in the area of relational data mining, a feature comparison with GUHA is presented. Both methods suffer from the explosion of the hypotheses space. This paper shows heuristic approach for GUHA method to deal with it, as well as other methods helping with the ...

The effort of data mining, especially in relation to association rules in real world business applications, is significantly important. Recently, association rules algorithms have been developed to cope with multidimensional data. In this paper we are concerned with mining association rules in data warehouses by focusing on its measurement of summarized data. We propose two algorithms: HAvg and...

We present a novel framework for set-valued data anonymization by partial suppression regardless of the amount of background knowledge the attacker possesses, and can be adapted to both space-time and quality-time trade-offs in a “pay-as-you-go” approach. While minimizing the number of item deletions, the framework attempts to either preserve the original data distribution or retain mineable us...

Since the last decades and due to the high capacity of storage, data is being increasingly available in information society, which has led to the need for valid tools for its modelling and analysis such as Knowledge Discovery in Databases (KDD) methods (known as data mining methods). However, many problems go along with the business application of data mining. In fact, the quality of the genera...

Purpose: With the development of information technology, online virtual learning community is on its way to become an important approach for people to construction and sharing of knowledge. Researches on virtual learning community are not only important to the establishment and management of virtual learning community itself, but are helpful for people’s quest for the future development of onli...

The purpose of this work is to mine closed frequent itemsets from transactional data streams using a sliding window model. An efficient algorithm IMCFI is proposed for Incremental Mining of Closed Frequent Itemsets from a transactional data stream. The proposed algorithm IMCFI uses a data structure called INdexed Tree(INT) similar to NewCET used in NewMoment[5]. INT contains an index table Item...

In wireless sensor networks, a significant amount of sensor readings sent from the sensors to the data processing point(s) may be lost or corrupted. In this research we propose a power-aware technique, called WARM (Window Association Rule Mining), to deal with such a problem. In WARM, to save battery power on sensors, instead of requesting the sensor nodes (MS), the readings of which are missin...

Data mining has been an area of increasing interests during recent years. The association rule discovery problem in particular has been widely studied. However, there are still some unresolved problems. For example, research on mining patterns in the evolution of numerical attributes is still lacking. This is both a challenging problem and one with significant practical application in business,...