This paper aims to explain the web-enabled tools for educational data mining. The proposed web-based tool developed using Asp.Net framework and php can be helpful for universities or institutions providing the students with elective courses as well improving academic activities based on feedback collected from students. In Asp.Net tool, association rule mining using Apriori algorithm is used wh...

One of the core tasks of Knowledge Discovery in Databases (KDD) is the mining of association rules. In this paper, truth values of association rules are discussed. Firstly, two knowledge bases of association rules are fixed, i.e., information system A and a fixed association rule (it’s confidence is 1), then based on Intuitionistic fuzzy special sets (IFSS) Representation of Rough Set, IFSS rep...

Data mining is an iterative and interactive process that explores and analyzes voluminous digital data to discover valid, novel, and meaningful patterns (Mohammed, 1999). Since digital data may have terabytes of records, data mining techniques aim to find patterns using computationally efficient techniques. It is related to a subarea of statistics called exploratory data analysis. During the pa...

Association rule algorithms typically only identify patterns that occur in the original form throughout the database. In databases which contain many small variations in the data, potentially important discoveries may be ignored as a result. In this paper, we describe an associate rule mining algorithm that searches for approximate association rules. Our ~AR approach allows data that approximat...

This paper proposes an algorithm to discover novel association rules, combined association rules. Compared with conventional association rule, this combined association rule allows users to perform actions directly. Combined association rules are always organized as rule sets, each of which is composed of a number of single combined association rules. These single rules consist of non-actionabl...

Data mining is concerned with developing algorithms and computational tools and techniques to help people extract patterns from data. In this paper an efficient data mining approach, which is based on fuzzy set theory and clonal selection algorithm, is proposed. The main motivation is to benefit from the global search performed by this kind of algorithms. Experimental results show the number of...

Although an overall knowledge discovery process consists of a distinct pre-processing stage followed by the data mining step, it seems that existing formal concept analysis (FCA) and association rules mining (ARM) approaches, dealing with many-valued contexts, mainly focus on the data mining stage. An “intelligent” pre-processing of input contexts is often absent in existing FCA/ARM approaches,...

Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into an important research area. Most conventional data-mining algorithms identify the relationships among transactions using binary values and 7nd rules at a single concept level. Transactions with quantitative values and items with hierarchy rela...

Association rule mining approaches traditionally generate rules based only on database contents, and focus on exact matches between items in transactions. In many applications, however, the utilization of some background knowledge, such as ontologies, can enhance the discovery process and generate semantically richer rules. Besides, fuzzy logic concepts can be applied on ontologies to quantify ...

Different studies have proposedmethods formining fuzzy association rules fromquantitative data, where themembership functions were assumed to be known in advance. However, it is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for mining fuzzy association rules. This paper thus presents a new fuzzy data-mining algorithm for extr...