In each and every field of science and technology Information science plays an important role. Sometimes information science is facing different types of problems to handle the data and information. Data Uncertainty is one of the challenging difficulties to handle. In past, there are several theories like fuzzy set, Rough set, Probability etc.to dealing with uncertainty. Soft set theory is the youngest theory to deal with uncertainty. In this paper we discussed how to find reducts. This paper focuses on how we can transform a sample data set to binary valued information system. We are also going to reduce the dimension of data set by using the binary valued information that results a better decision.

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Clustering is one of the most effective methods for summarizing and analyzing datasets that are collection of data objects similar or dissimilar in nature. Clustering aims at finding groups, or clusters, of objects with similar attributes. Most clustering methods work efficiently for low dimensional data since distance measures are used to find dissimilarities between objects. High dimensional data, however, may contain attributes which are not required for defining clusters and irrelevant dimension may produce noise and will hide the clusters that are required to be created. The discovery of groups of objects that are highly similar within some subsets of relevant attributes becomes an important but challenging task. In this paper we provide a short introduction to various approaches and challenges for high-dimensional data clustering.

A network data set may contain a huge amount of data and processing this huge amount of data is one of the most challenges task for network based intrusion detection system (IDS). Normally these data contain lots of redundant and irrelevant features. Feature selection approaches are used to extract the relevant features from the original data to improve the efficiency or accuracy of IDS. In this paper an effective feature selection approaches are used for the NSL KDD data set. The performance of the used classifiers measure and compared with each other.

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