Hindawi Publishing Corporation International Journal of Combinatorics Volume 2011, Article ID 893061, 10 pages doi:10.1155/2011/893061 Research Article Some More Results on IF Soft Rough Approximation Space Sharmistha Bhattacharya (Halder) 1 and Bijan Davvaz 2 1 Department of Mathematics, Tripura University, Suryamaninagar, Tripura 799130, India 2 Department of Mathematics, Yazd University, Yazd 89195-741, Iran Correspondence should be addressed to Bijan Davvaz, davvaz@yazduni.ac.ir Received 6 September 2011; Accepted 20 November 2011 Academic Editor: Toufik Mansour Copyright q 2011 S. Bhattacharya Halder and B. Davvaz. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Fuzzy sets, rough sets, and later on IF sets became useful mathematical tools for solving various decision making problems and data mining problems. Molodtsov introduced another concept soft set theory as a general frame work for reasoning about vague concepts. Since most of the data collected are either linguistic variable or consist of vague concepts so IF set and soft set help a lot in data mining problem. The aim of this paper is to introduce the concept of IF soft lower rough approximation and IF upper rough set approximation. Also, some properties of this set are studied, and also some problems of decision making are cited where this concept may help. Further research will be needed to apply this concept fully in the decision making and data mining problems. 1. Introduction Data mining is a technique of extracting meaningful information from large and mostly un- organized data banks. Data mining is one of the areas in which rough set is widely used. Data mining is the process of automatically searching large volumes of data for patterns using tools such as classifications, association, rule mining, and clustering. The rough set theory is a well understood format framework for building data mining models in the form of logic rules on the bases of which it is possible to issue predictions that allow classifying new cases. In general whenever data are collected they are linguistic variables. Not only this, the answers are not always in Yes/No form. So, in this case to deal with such type of data IF set is a very important tool. Data are in most of the cases a relation between object and attribute. Soft set is an important tool to deal with such types of data. So, throughout this paper a combined approach of soft set, IF set, rough set is studied. Further study is required to find the application of this concept in the field of data mining.