association rules are among important techniques in data mining which are used for extracting hidden patterns and knowledge in large volumes of data. association rules help individuals and organizations take strategic decisions and improve their business processes. extracted association rules from a database contain important and confidential information that if published, the privacy of indivi...

With the exponential growth of the number of documents available on the Internet, automatic feature selection approaches are increasingly important for the preprocessing of textual documents for data mining. Feature selection, which focuses on identifying relevant data, can help reduce the workload of processing huge amounts of data as well as increase the accuracy for the subsequent data minin...

No learner is generally better than another learner. If a learner performs better than another learner on some learning situations, then the first learner usually performs worse than the second learner on other situations. In other words, no single learning algorithm can perform well and uniformly outperform other algorithms over all learning or data mining tasks. There is an increasing number ...

Feature selection is a term usually use in data mining to demonstrate the tools and techniques available for reducing inputs to a convenient size for processing and analysis. In this paper authors has reviewed the literature of feature selection algorithms such as well known attributes selection methods of FCBF, ReliefF, SVM-RFE, Random selection. This review of literature focuses on how featur...

Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students’ performance. The performance in higher education in India is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore it is essential to develop predictive data mining model for stude...

The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show low performance for data that inherently suffer from noise, resulting in temporally inconsistent clusters. We propose an evolutionary clusteri...

Instance selection methods are very useful data mining tools for dealing with large data sets. There exist many instance selection algorithms capable for significant reduction of training data size for particular classifier without generalization degradation. In opposition to those methods, this paper focuses on general pruning methods which can be successfully applied for arbitrary classificat...