Often, the explanatory power of a learned model must be traded off against model performance. In the case of predicting runaway software projects, we show that the twin goals of high performance and good explanatory power are achievable after applying a variety of data mining techniques (discrimination, feature subset selection, rule covering algorithms). This result is a new high water mark in...

Current industries data’s are stored in relation structures. In usual approach to mine these data, we often use to join several relations to form a single relation using foreign key links, which is known as flatten. Flatten may cause troubles such as time consuming, data redundancy and statistical skew on data. Hence, the critical issues arise that how to mine data directly on numerous relation...

Feature selection is a pre-processing step, used to improve the mining performance by reducing data dimensionality. Even though there exists a number of feature selection algorithms, still it is an active research area in data mining, machine learning and pattern recognition communities. Many feature selection algorithms confront severe challenges in terms of effectiveness and efficiency, becau...

In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document are first preprocessed to transform to either an Indexed XML Tree (IX-tree) or Multi-relational Databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection and AR mining. Concepts that...

These days, network traffic is increasing due to the increasing use of smart devices and the Internet. Amount of the intrusion detection studies focused on feature selection or reduction because some of the features are irrelevant and redundant which results lengthy detection process and degrades the performance of an intrusion detection system (IDS). The purpose of this study is to identify im...

Huge amount data on the internet are in unstructured texts can‟t simply be used for further processing by computer , therefore specific processing method and algorithm require to extract useful pattern. Text mining is process to extract information from the unstructured data. Text classification is task of automatically sorting set of document into categories from predefined set. A major diffic...

An investigation has been conducted on frequently used feature selection methods in data pre-processing for data mining. After identifying the weaknesses and strengths of each of the approaches, we propose modifications to the ReliefF methods by: (1) using a kNN model as the starter selection, aimed at choosing a set of more meaningful representatives to replace the original data for feature se...