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  • × author_ss:"Deogun, J.S."
  1. Bhatia, S.K.; Deogun, J.S.; Raghavan, V.V.: Conceptual query formulation and retrieval (1995) 0.00 
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    Abstract
    In this paper, we advance a technique to develop a user profile for information retrieval through knowledge acquisition techniques. The profile bridges the discrepancy between user-expressed keywords and system-recognizable index terms. The approach presented in this paper is based on the application of personal construct theory to determine a user's vocabulary and his/her view of different documents in a training set. The elicited knowledge is used to develop a model for each phrase/concept given by the user by employing machine learning techniques. Our model correlates the concepts in a user's vocabulary to the index terms present in the documents in the training set. Computation of dependence between the user phrases also contributes in the development of the user profile and in creating a classification of documents. The resulting system is capable of automatically identifying the user concepts and query translation to index terms computed by the conventional indexing process. The system is evaluated by using the standard measures of precision and recall by comparing its performance against the performance of the smart system for different queries.
    Type
    a
  2. Deogun, J.S.: Feature selection and effective classifiers (1998) 0.00 
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    Abstract
    Develops and analyzes 4 algorithms for feature selection in the context of rough set methodology. Develops the notion of accuracy of classification that can be used for upper or lower classification methods and defines the feature selection problem. Presents a discussion of upper classifiers and develops 4 features selection heuristics and discusses the family of stepwise backward selection algorithms. Analyzes the worst case time complexity in all algorithms presented. Discusses details of the experiments and results of using a family of stepwise backward selection learning data sets and a duodenal ulcer data set. Includes the experimental setup and results of comparison of lower classifiers and upper classiers on the duodenal ulcer data set. Discusses exteded decision tables
    Footnote
    Contribution to a special issue devoted to knowledge discovery and data mining
    Type
    a
  3. Raghavan, V.V.; Deogun, J.S.; Sever, H.: Knowledge discovery and data mining : introduction (1998) 0.00 
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    Abstract
    Defines knowledge discovery and database mining. The challenge for knowledge discovery in databases (KDD) is to automatically process large quantities of raw data, identifying the most significant and meaningful patterns, and present these as as knowledge appropriate for achieving a user's goals. Data mining is the process of deriving useful knowledge from real world databases through the application of pattern extraction techniques. Explains the goals of, and motivation for, research work on data mining. Discusses the nature of database contents, along with problems within the field of data mining
    Footnote
    Contribution to a special issue devoted to knowledge discovery and data mining
    Type
    a

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