Mining Closed Interesting Subspaces to Discover Conducive Living Environment of Migratory Animals

This paper presents the suitability of subspace clustering techniques to identify the conducive living environment of migratory animals given the geographical and weather conditions prevailing at various locations where the animals thrive. The set of collaborative weather and geographical conditions prevailing at different locations where animals move define the conducive living environment/conditions of animals and hence their accessibility in turn influence the migration behavior of animals. The concept of closed interesting subspaces in density divergence context for multidimensional data is proposed by the authors to model the conducive living conditions of migratory animals. A grid-based subspace mining algorithm namely SCHISM which is originally meant for extracting the maximal interesting subspaces was adapted for finding closed interesting subspaces. Migratory Burchell’s Zebra movement data collected from MoveBank was used for this analysis purpose.

[1]  Mohammed J. Zaki,et al.  SCHISM: a new approach to interesting subspace mining , 2005, Int. J. Bus. Intell. Data Min..

[2]  Jiawei Han,et al.  The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data , 2013, Movement Ecology.

[3]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[4]  Hans-Peter Kriegel,et al.  Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering , 2009, TKDD.

[5]  Ira Assent,et al.  INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[6]  Hans-Peter Kriegel,et al.  A generic framework for efficient subspace clustering of high-dimensional data , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[7]  Gil Bohrer,et al.  In search of greener pastures: Using satellite images to predict the effects of environmental change on zebra migration , 2013 .

[8]  Jonathan Goldstein,et al.  When Is ''Nearest Neighbor'' Meaningful? , 1999, ICDT.

[9]  Yi Zhang,et al.  Entropy-based subspace clustering for mining numerical data , 1999, KDD '99.

[10]  Hans-Peter Kriegel,et al.  Density-Connected Subspace Clustering for High-Dimensional Data , 2004, SDM.

[11]  Ira Assent,et al.  DUSC: Dimensionality Unbiased Subspace Clustering , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[12]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[13]  Jiawei Han,et al.  Mining periodic behaviors of object movements for animal and biological sustainability studies , 2011, Data Mining and Knowledge Discovery.

[14]  Ming-Syan Chen,et al.  Density Conscious Subspace Clustering for High-Dimensional Data , 2010, IEEE Transactions on Knowledge and Data Engineering.

[15]  M. Wikelski,et al.  Vegetation dynamics drive segregation by body size in Galapagos tortoises migrating across altitudinal gradients. , 2013, The Journal of animal ecology.

[16]  M. Wikelski,et al.  Seed dispersal by Galápagos tortoises , 2012 .

[17]  Arthur Zimek,et al.  A survey on enhanced subspace clustering , 2013, Data Mining and Knowledge Discovery.

[18]  Ira Assent,et al.  Scalable density-based subspace clustering , 2011, CIKM '11.

[19]  Tal Avgar,et al.  Environmental and individual drivers of animal movement patterns across a wide geographical gradient. , 2013, The Journal of animal ecology.