Rough Set Approach for the Classification of Advertisement in the Development of Business Establishment

In the current age business establishments are basically depends upon advertisement to attain success . In this paper we consider different forms of advertisements then using rough set concept, we find the best possible forms of advertisement. To develop this concept we consider 1000 samples initially and applying correlation techniques the number reduces to 20 which appears to be dissimilar with respect to advertisements initially. We classified the entire paper in to four section , section 1 deals with the literature review and in the section 2 deals with the experiment on the data which we collected from different sources and in last two section deals with the algorithm which we develop using rough set concept and validation of the algorithm using statistical test .

[1]  Erkki K. Laitinen,et al.  Survival analysis as a tool for company failure prediction , 1991 .

[2]  J. Courtis.  MODELLING A FINANCIAL RATIOS CATEGORIC FRAMEWORK , 1978 .

[3]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[4]  Constantin Zopounidis,et al.  DEVELOPING A MULTICRITERIA KNOWLEDGE-BASED DECISION SUPPORT SYSTEM FOR THE ASSESSMENT OF CORPORATE PERFORMANCE AND VIABILITY: THE FINEVA SYSTEM , 1996 .

[5]  James V. Hansen,et al.  Inducing rules for expert system development: an example using default and bankruptcy data , 1988 .

[6]  E. Altman.  The success of business failure prediction models: An international survey , 1984 .

[7]  Niall M. Adams,et al.  Data Mining for Fun and Profit , 2000 .

[8]  Edward I. Altman,et al.  Application of Classification Techniques in Business, Banking and Finance. , 1983 .

[9]  Padhraic Smyth,et al.  Statistical inference and data mining , 1996, CACM.

[10]  Graham J. Williams,et al.  Data Mining: Theory, Methodology, Techniques, and Applications (Lecture Notes in Computer Science) , 2006 .

[11]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[12]  Mark J Funt.  Financial ratios. , 2009, Pennsylvania dental journal.

[13]  Hing-Yan Lee,et al.  Visualization Support for Data Mining , 1996, IEEE Expert.

[14]  Jaap Spronk,et al.  The Application of the Multi-Factor Model in the Analysis of Corporate Failure , 1998 .

[15]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[16]  W. Beaver.  Financial Ratios As Predictors Of Failure , 1966 .

[17]  Hongjun Lu,et al.  Effective Data Mining Using Neural Networks , 1996, IEEE Trans. Knowl. Data Eng..

[18]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[19]  Vashist Renu,et al.  Rule Generation based on Reduct and Core: A Rough Set Approach , 2011 .

[20]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[21]  Ananth Grama,et al.  Data Mining: From Serendipity to Science - Guest Editors' Introduction , 1999, Computer.

[22]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[23]  H. Frydman,et al.  Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress , 1985 .

[24]  Katherine Schipper,et al.  Application of Classification Techniques in Business, Banking and Finance. , 1983 .

[25]  Yash P. Gupta,et al.  LINEAR GOAL PROGRAMMING AS AN ALTERNATIVE TO MULTIVARIATE DISCRIMINANT ANALYSIS: A NOTE , 1990 .

[26]  Constantin Zopounidis,et al.  A Multicriteria Approach for the Analysis and Prediction of Business Failure in Greece , 1998 .

[27]  Andrzej Skowron,et al.  Rough-Fuzzy Hybridization: A New Trend in Decision Making , 1999 .

[28]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .