Knowledge exploration from the large set of data, generated as a result of the various data processing activities due to data mining only. Frequent Pattern Mining is a very important undertaking in data mining. Apriori approach applied to generate frequent item set generally espouse candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how t...

This paper presents Weka4WS, a framework that extends the Weka toolkit for supporting distributed data mining on Grid environments. Weka4WS adopts the emerging Web Services Resource Framework (WSRF) for accessing remote data mining algorithms and managing distributed computations. The Weka4WS user interface is a modified Weka Explorer environment that supports the execution of both local and re...

Kansei or Affective Engineering is a relatively new research field in the West. Linköping University in Sweden has been pioneering research and application within Kansei Engineering since 1999. Several companies have cooperated in different Kansei studies, for example Toyota/BT, Volvo, Saab, Scania. In some of the early studies, there were difficulties to apply the methodology. Reasons for this...

Data mining tools may be computationally demanding, so there is an increasing interest on parallel computing strategies to improve their performance. The popularization of Graphics Processing Units (GPUs) increased the computing power of current desktop computers, but desktop-based data mining tools do not usually take full advantage of these architectures. This paper exploits an approach to im...

TAMPERE UNIVERSITY OF TECHNOLOGY Master's Degree Programme in Information Technology MINAEV, GEORGY: Comparing GUHA and Weka Methods in Data Mining Master of Science Thesis, 67 pages, 0 Appendix pages 07 November 2012 Major: Mathematics Examiner: Associate Professor Esko Turunen and Professor Ari Visa

The Weka4GML framework has been designed to meet the requirements of distributed data mining. In this paper, we present the Weka4GML architecture based on WSRF technology for developing meta-learning methods to deal with datasets distributed among Data Grid. This framework extends the Weka toolkit to support distributed execution of data mining methods, like meta-learning. The architecture and ...

The Waikato Environment for Knowledge Analysis (Weka) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning and data mining algorithms. Weka is freely available on the World-Wide Web and accompanies a new text on data mining [1] which documents and fully explains all the algorithms it contains. Applications written using the Weka class libraries ...

Kansei Engineering was developed as a consumer-oriented technology for new product development. It is defined as "translating technology of a consumer's feeling and image for a product into design elements". Kansei Engineering (KE) technology is classified into three types, KE Type I, II, and III. KE Type I is a category classification on the new product toward the design elements. Type II util...