Meghana Deshmukh et al , International Journal of Computer Science and Mobile Computing, Vol.3 Issue.1, January- 2014, pg. 519-525 © 2014, IJCSMC All Rights Reserved 519 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 3, Issue. 1, January 2014, pg.519 – 525 RESEARCH ARTICLE Predictive Data Mining: A Generalized Approach Meghana Deshmukh Lecturer, Dept. of Computer Science and Engineering, Prof. Ram Meghe Institute of Technology & Research Badnera, Amravati, India meghnadeshmukh9@gmail.com Prof. S. P. Akarte Asst. Prof. Dept. of Computer Science and Engineering, Prof. Ram Meghe Institute of Technology & Research Badnera, Amravati, India s_akarte25@rediffmail.com Abstract— In this paper, we included the ambitious task of formulating a general framework of data mining. We explained that the framework should fulfil. It should elegantly handle different types of data, different data mining tasks, and different types of patterns/models. We also discuss data mining languages and what they should support: this includes the design and implementation of data mining algorithms, as well as their composition into nontrivial multi step knowledge discovery scenarios relevant for practical application. We proceed by laying out some basic concepts, starting with (structured) data and generalizations (e.g., patterns and models) and continuing with data mining tasks and basic components of data mining algorithms (i.e., refinement operators, distances, features and kernels). We next discuss how to use these concepts to formulate constraint-based data mining tasks and design generic data mining algorithms. Finally this paper discussed about these components would fit in the overall framework and in particular into a language for data mining and knowledge discovery. Keywords— data mining; data mining cycle; patterns; data mining methods; tasks 1. INTRODUCTION When knowledge discovery in databases (KDD) and data mining have enjoyed great popularity and success in recent years, there is a distinct lack of a generally accepted framework for data mining. The present lack of such a framework is perceived as an obstacle to the further development of the field. Much of the current research in data mining is about mining complex data, e.g., text mining, link mining, mining social network data, web mining, multi-media data mining. As the complexity of the data analyzed grows, more expressive formalisms are needed to represent patterns found in the data. The use of such formalisms has been proposed within relational data mining and statistical relational learning; these are now used increasingly more often in link mining, web mining and mining of