• Login
    View Item 
    •   etd@IISc
    • Division of Electrical, Electronics, and Computer Science (EECS)
    • Computer Science and Automation (CSA)
    • View Item
    •   etd@IISc
    • Division of Electrical, Electronics, and Computer Science (EECS)
    • Computer Science and Automation (CSA)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A Knowledge-Based Approach To Pattern Clustering

    View/Open
    knowledge.pdf (4.083Mb)
    Date
    2005-03-11
    Author
    Shekar, B
    Metadata
    Show full item record
    Abstract
    The primary objective of this thesis is to develop a methodology for clustering of objects based on their functionality typified by the notion of concept. We begin by giving a formal definition of concept. By assigning a functional interpretation to the underlying concept, we demonstrate the applicability of the functionally interpreted concept for clustering objects. This functional interpretation leads us to identifying two classes of concepts, namely, the Necessary class and the Quality-Improvement class. Next, we categorize the functional cohesiveness among objects into three different classes. Further, we axiomatize the restrictions imposed, on the execution of functions of objects, by the non-availability of sufficient resources. To facilitate describing functional clusters in a succinct manner, we define connectives that capture the imposed restrictions. Also we justify the adequacy of these connectives for describing functional clusters. We then propose a suitable data structure to represent the functionally interpreted concept, and develop an algorithm to perform this axiomatic functional partitioning of objects. We illustrate the functional partitioning of objects through a real-world example. We formally establish the invariance of the resulting cluster descriptions, with respect to the order in which the given set of objects is examined. This invariance would facilitate parallel implementations of the proposed methodology. We then analyze different functional cluster configurations from a structural viewpoint. In doing so, we identify the presence of a specific property among certain cluster configurations. We also state a sufficient condition for the presence of this property in any cluster. A separate class of concepts, namely the Concept Transformer class, displaying certain properties, is identified and studied in detail. We also demonstrate its applicability to functional clustering. Finally, we examine a knowledge-based pattern synthesis problem from a functional angle as a significant application of the functional interpretation of concept and associated data structures. Here, we show that a concept, from the functional view-point, can be viewed as the synthesis of various other concepts; the synthesis is an outcome of a knowledge-based goal-directed pattern-matching activity. The proposed methodology has the potential to cluster objects that imply functions by virtue of their physical properties.
    URI
    https://etd.iisc.ac.in/handle/2005/86
    Collections
    • Computer Science and Automation (CSA) [392]

    etd@IISc is a joint service of SERC & J R D Tata Memorial (JRDTML) Library || Powered by DSpace software || DuraSpace
    Contact Us | Send Feedback | Thesis Templates
    Theme by 
    Atmire NV
     

     

    Browse

    All of etd@IIScCommunities & CollectionsTitlesAuthorsAdvisorsSubjectsBy Thesis Submission DateThis CollectionTitlesAuthorsAdvisorsSubjectsBy Thesis Submission Date

    My Account

    LoginRegister

    etd@IISc is a joint service of SERC & J R D Tata Memorial (JRDTML) Library || Powered by DSpace software || DuraSpace
    Contact Us | Send Feedback | Thesis Templates
    Theme by 
    Atmire NV