Parasaran Raman
PhD Student. Graduated in Fall 2013. First Employment: Yahoo.


[ Book Chapter  Journal  Conference  Workshop  Tech Report]

Journal

2013

  • Power to the Points: Validating Data Memberships in Clusterings (Project Website)
    By Parasaran Raman,   Suresh Venkatasubramanian,   
    Vol.abs/1305.4757, CoRR (CORR 2013),  2013.
    Abstract

    • 2012

  • Fast Multiple Kernel Learning With Multiplicative Weight Updates (Project Website)
    By John Moeller,   Parasaran Raman,   Avishek Saha,   Suresh Venkatasubramanian,   
    Vol.abs/1206.5580, CoRR (CORR 2012),  2012.
    Abstract

    • Conference

    2014

  • A Geometric Algorithm for Scalable Multiple Kernel Learning. (Project Website)
    By John Moeller,   Parasaran Raman,   Suresh Venkatasubramanian,   Avishek Saha,   
    In Proceedings of AISTATS (AISTATS 2014),  pages 633-642,  2014.
    Abstract

    • 2013

  • Power to the Points: Validating Data Memberships in Clusterings. (Project Website)
    By Parasaran Raman,   Suresh Venkatasubramanian,   
    In Proceedings of ICDM (ICDM 2013),  pages 617-626,  2013.
    Abstract

    • 2011

  • Spatially-Aware Comparison and Consensus for Clusterings
    By Jeff M. Phillips,    Parasaran Raman,    and Suresh Venkatasubramanian
    In Proceedings of 10th SIAM Intenational Conference on Data Mining (SDM 2011),  pages 307-318,  Mesa, Arizona, USA,   April,  2011.
    Abstract

    This paper proposes a new distance metric between clusterings that incorporates information about the spatial distribution of points and clusters. Our approach builds on the idea of a Hilbert space-based representation of clusters as a combination of the representations of their constituent points. We use this representation and the underlying metric to design a spatially-aware consensus clustering procedure. This consensus procedure is implemented via a novel reduction to Euclidean clustering, and is both simple and efficient. All of our results apply to both soft and hard clusterings. We accompany these algorithms with a detailed experimental evaluation that demonstrates the efficiency and quality of our techniques.

    • Workshop

    2011

  • Generating a Diverse Set of High-Quality Clusterings
    by Jeff M. Phillips,    Parasaran Raman,    Suresh Venkatasubramanian
    In Proceedings of the the 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings, in conjunction with the ECML/PKDD 2011 (MultiClust 2011), pages ??-??, Athens, Greece,  September,  2011.  .
    Abstract

    We provide a new framework for generating multiple good quality partitions (clusterings) of a single data set. Our approach decomposes this problem into two components, generating many high-quality partitions, and then grouping these partitions to obtain k representatives. The decomposition makes the approach extremely modular and allows us to optimize various criteria that control the choice of representative partitions.