Search results for: B. K. Tripathy
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In recent times enumerable number of clustering algorithms have been developed whose main function is to make sets of objects having almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and so have...
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Social Network Analysis and Mining > 2016 > 6 > 1 > 1-11
Rapid growth and development of social networks has attracted the interest of the scientific community to utilize these huge datasets for research purpose. However, preserving the privacy of the users in the published data has also become an important concern. An adversary with very little background knowledge about the actors can extract personal information from the published data. To prevent such...
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Lecture Notes in Computer Science > Next-Generation Applied Intelligence > Special Session: Mining Interesting Knowledge (I) > 777-786
Approximations of classifications, introduced and studied by Grzymala Busse [4], and approximations of sets are two different issues (Pawlak [9]) and the equivalence classes of approximate classifications can not be arbitrary sets. Busse [4] had established properties of approximations of classifications which were recently extended to necessary and sufficient type theorems by Tripathy et al. [12]...
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Studies in Computational Intelligence > Rough Set Theory: A True Landmark in Data Analysis > Theoretical Contributions to Rough Set Theory > 3-44
Summary In the past few years the original concept of rough sets, as introduced by Pawlak [26] has been extended in many different directions. Some of these extensions are obtained by relaxing the requirement of the basic relations to be equivalence relations [14,15,31,32,34,35,37,38].That is by dropping the requirement of transitivity or symmetry. One such approach is to replace the equivalence relations...
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Studies in Computational Intelligence > Rough Set Theory: A True Landmark in Data Analysis > Theoretical Contributions to Rough Set Theory > 85-133
Summary In this chapter we mainly focus on the study of some topological aspects of rough sets and approximations of classifications. The topological classification of rough sets deals with their types. We find out types of intersection and union of rough sets, New concepts of rough equivalence (top, bottom and total) are defined, which capture approximate equality of sets at a higher level than rough...
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Soft set theory introduced by Molodtsov is a new mathematical approach to handle the uncertainty problems. It is a family of subsets associated with each parameter in a soft space. Hybrid models have been found to be more useful than the individual components. Earlier fuzzy set and soft set were combined to form fuzzy soft sets and similarly intuitionistic fuzzy soft sets (IFSS) were introduced. Like...
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Decision-making is a technique which all of us do every day in different contexts. In Decision-making several alternates are there from which the best one is to be selected. But the alternates may not be crisp many times. So, uncertainty based techniques are necessary to solve these problems. Among the different models of uncertainty, we have fuzzy sets, intuitionistic fuzzy sets and soft sets. There...
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Imprecision based data clustering algorithms have gained a lot of importance these days because of the imprecise character of modern day databases. Some such algorithms are the rough c-means (RCM), fuzzy c-means (FCM) and their hybrid versions. Li et al used the decision theoretic rough set (DTRS) model by the way improving the RCM. In their approach they have used a notion called loss function to...
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Application of clustering algorithms for investigating real life data has concerned many researchers and vague approaches or their hybridization with other analogous approaches has gained special attention due to their great effectiveness. Recently, rough intuitionistic fuzzy c-means algorithm has been proposed by Tripathy et al [3] and they established its supremacy over all other algorithms contained...
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We propose an (a, k) anonymity model based on the eigenvector centrality value of the nodes present in the raw graph and further extend it to propose (a, l) diversity model and recursive (a, c, l) diversity model which can handle the protection of the sensitive attributes associated with a particular actor. For anonymization purpose, we applied noise node addition technique to generate the anonymized...
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Several data clustering techniques have been developed in literature. It has been observed that the algorithms developed by using imprecise models like rough sets, fuzzy sets and intuitionistic fuzzy sets have been better than the crisp algorithms. Also, the hybrid models provide far better clustering algorithms than the individual models. Several such models have been developed by using a combination...
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A fuzzy algorithm is presented for image segmentation of 2D gray scale images whose quality have been degraded by various kinds of noise. Traditional Fuzzy C Means (FCM) algorithm is very sensitive to noise and does not give good results. To overcome this problem, a new fuzzy c means algorithm was introduced [1] that incorporated spatial information. The spatial function is the sum of all the membership...
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The explosive growth of data, images in the World Wide Web makes it critical to the information retrievals. Image retrieval has been recognized as an elementary problem in the retrieval tasks and this exercise has got a wide attention based on the underlying domain characteristics. For instance, in social media data encompasses of noisy, diverse, heterogeneous, interconnected data. To confront these...
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Clustering is a familiar concept in the realm of Data mining and has wide applications in areas like image processing, pattern recognition and rule generation. Uncertainty in present day databases is a common feature. In order to handle these datasets, several clustering algorithms have been formulated in the literature. The first one being the Fuzzy C-Means (FCM) algorithm and it was followed by...
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Understanding the Learner requirements is an important aspect of any learning environment as it helps to recommend the LOs in a more personalized manner. With the growing demand for MOOCs offered by coursera, edx, etc. the learner information plays a vital role in understanding the extent to which the learners can gain out of such courses. The Learning Management Systems (LMS) across the web uses...
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Ensuring privacy to sensitive data is becoming an important criterion in the data access policies of governments and corporations. Providing security to data using non-cryptographic based techniques has been in use for a long time. They are used to provide security to Health data, finance data and the like. Data are distorted using various approaches to hide sensitive information and provide privacy...
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Content based recommender systems have the drawback of recommending only similar items to a user's particular taste, irrespective of the item's popularity. Collaborative Filtering based systems face the problem of data sparsity and expensive parameter training. In this paper, a combination of content-based, model and memory-based collaborative filtering techniques is used in order to remove these...
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The notion of rough sets [4] was used by Novotny and Pawlak [1, 2, 3] to introduce the concept of rough equality, in order to incorporate user knowledge in determining the equality of sets, which is a natural phenomenon in day to day life. Even this notion was shown to be deficient and was extended by Tripathy et al [6, 9] to define the notion of rough equivalence of sets, which is more suitable in...
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The notion of Fuzzy sets introduced by Zadeh has been found to be a fruitful model in dealing with uncertainty in data. In order to define the cardinality of fuzzy sets the notion of sigma count was introduced by DeLuca and Termini. Cardinality of a fuzzy set as a fuzzy number, which is a simple but more natural extension of the approach of DeLuca and Termini was suggested by Zadeh. Our objective...
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There have been three approaches to find the measures of fuzzy sets, which extend the count function for crisp sets. The first one is due to Deluca and Termini; second one a measure theoretic approach due to Zadeh and a bag theoretic approach due to Tripathy et al. Intuitionistic fuzzy sets are generalizations of fuzzy sets. So, efforts have been made recently to extend the above three approaches...
