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Geometric & Network Based Summarization

Geometric & Network Based Summarization. Atanu Roy Akash Agrawal. Problem Statement. Input A spatial network Set of activities and their locations Number of clusters ( k ) Output k sets of disjoint clustered activities (geometric and route based) Objective

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Geometric & Network Based Summarization

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  1. Geometric & Network Based Summarization Atanu Roy AkashAgrawal

  2. Problem Statement • Input • A spatial network • Set of activities and their locations • Number of clusters (k) • Output • k sets of disjoint clustered activities (geometric and route based) • Objective • Maximize the density and the coverage of the clusters • Constraint • Correct • ; where are the number of geometric and network based clusters

  3. Running Example

  4. Motivation & Challenges • Motivation • Crime Detection and Prevention • Disaster Response • Challenges • K-means (P-median) & SNAS are NP_hard. [Reese2005, Unpublished] • Decision to use either of the solution is proven (in this work) to be an exponential problem.

  5. Novel

  6. Better

  7. Proposed Approach • Implement crimestat and KMR • Use each to have put k clusters on the network (total 2k). • Try eliminating k clusters one by one • Use coverage measure to eliminate geometric based cluster and density to eliminate network based ones.

  8. Thank You • References • Dev Oliver, AbdussalamBannur, James M. Kang, ShashiShekhar, and Renee Bousselaire, A k-main routes approach to spatial network activity summarization: A summary of results, ICDM Workshops, 2010, pp. 265 - 272. • J. Reese (2006) Solution methods for the p-median problem: an annotated bibliography. Networks 48(3):125–142

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