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Different Methods for Cluster’s Representation and Their Impact on the Effectiveness of Searching Through Such a Structure

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Computational Collective Intelligence (ICCCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11056))

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Abstract

In this paper the topic of clustering and searching through clusters generated from real-world knowledge bases is discussed. Authors analyze three methods of cluster’s representatives creation, focusing on their advantages and flaws. What is more the authors introduce a concept of forward-chaining inference which uses a density-based DBSCAN algorithm to generate cluster of rules and speed up the whole inference process. The experiments were conducted on real-world knowledge bases with a relatively large number of rules to evaluate the efficiency of the proposed approach.

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Notes

  1. 1.

    A cluster is considered relevant when its similarity (to the set of facts) is above a given threshold.

  2. 2.

    When using index structures, like R-trees [2], the average computational complexity is about \(O(\log n)\), where n is the number of instances [2].

  3. 3.

    The DBSCAN algorithm distinguishes core (objects in the center of the cluster) and border points (objects near the cluster’s border), and uses specific notations of reachability and connectedness to form a cluster, which are discussed in [2].

  4. 4.

    Because gaining access to large, real-world knowledge bases is very difficult.

  5. 5.

    More information about the structure and experimental evaluation concerning these knowledge bases can be found in [17].

  6. 6.

    The measure was chosen, because it is simple to comprehend and does not have a significant impact on the DBSCAN execution time.

  7. 7.

    Although 3 types of representatives were discussed in this paper, only the first two approaches were used in the experiments, because for the last one it is difficult to specify how many threshold values should be considered and this optimal threshold (percentage of rules considered) may vary for every knowledge base.

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Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, ul. Bȩdzińska 39, 41-200, Sosnowiec, Poland

    Tomasz Xiȩski & Agnieszka Nowak-Brzezińska

Authors
  1. Tomasz Xiȩski
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  2. Agnieszka Nowak-Brzezińska
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Correspondence to Tomasz Xiȩski .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Elias Pimenidis

  3. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Zaheer Khan

  4. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Xiȩski, T., Nowak-Brzezińska, A. (2018). Different Methods for Cluster’s Representation and Their Impact on the Effectiveness of Searching Through Such a Structure. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11056. Springer, Cham. https://doi.org/10.1007/978-3-319-98446-9_27

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  • DOI: https://doi.org/10.1007/978-3-319-98446-9_27

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