http://www.iaeme.com/IJCET/index.asp 67 editor@iaeme.com International Journal of Computer Engineering & Technology (IJCET) Volume 8, Issue 5, Sep-Oct 2017, pp. 67–77, Article ID: IJCET_08_05_008 Available online at http://www.iaeme.com/ijcet/issues.asp?JType=IJCET&VType=8&IType=5 Journal Impact Factor (2016): 9.3590(Calculated by GISI) www.jifactor.com ISSN Print: 0976-6367 and ISSN Online: 0976–6375 © IAEME Publication KNOWLEDGE DATA MAP - A FRAMEWORK FOR THE FIELD OF DATA MINING AND KNOWLEDGE DISCOVERY M.A. Shanti Research Scholar, Department of Computer Science and Engineering, PRIST University, Thanjavur, India K. Saravanan DEAN, Faculty of Computer Science, PRIST University, Thanjavur, India ABSTRACT Knowledge Discovery and Data Mining is a versatile and associative area focusing upon procedures and approaches for extracting useful cognition from data. The constant agile development of online data due to the increase in the usage of Internet and the prevalent employment of databases have created an immense need for Knowledge Discovery and Data Mining methodologies. Comparatively very sparse research has been published about the theoretical foundations involving knowledge discovery and data mining. This paper proposes a framework which also serves as an efficient ground work that attempts to define the discipline and major divisions of Data Mining and Knowledge Discovery. Grounded theory is a standardized procedural program in the social sciences involving the arrangement of theory through the reasoning of data. Grounded Theory is a provisional methodology which operates almost in a turn around fashion from social science research. The proposed framework is built upon by following a Grounded Theory approach. For this study, we have considered a substantial amount of Data Mining and Knowledge Discovery literature s, which is not limited to the domain related journals, various Data Mining and Knowledge Discovery conference proceedings and dissertations. This study develops a framework of four main areas for the field: (1)Data Mining and Knowledge Discovery foundation elements, (2) Data Mining and Knowledge Discovery learning procedures, (3) Data Mining and Knowledge Discovery software & systems and (4) Data mining undertakings. The aforementioned areas form the central theme of this paper. Key word: Data mining and knowledge discovery, Data mining systems, Learning procedures, Grounded theory, Framework approach. Cite this Article: M.A. Shanti and K. Saravanan, Knowledge Data Map - A Framework for the Field of Data Mining and Knowledge Discovery. International Journal of Computer Engineering & Technology, 8(5), 2017, pp. 67–77. http://www.iaeme.com/ijcet/issues.asp?JType=IJCET&VType=8&IType=5