We propose an efficient algorithm to mine positive and negative patterns in large spatial databases. The algorithm is based on exploiting a complementarity property for a certain support-like measure. This property guarantees that if a positive k-pattern is "frequent" then O (k) related negative patterns will be infrequent. For the traditional support measure this complementarity property holds true only when the minimum support is over fifty percent We also confirm the correctness of our approach using Ripley's K-Function, a standard tool in spatial statistics for analyzing point patterns. Extensive experimentation on data extracted from the Sloan Digital Sky Survey (SDSS) database demonstrates the utility of our approach to large scale data exploration.
|Number of pages||10|
|State||Published - 2005|
|Event||5th SIAM International Conference on Data Mining, SDM 2005 - Newport Beach, CA, United States|
Duration: Apr 21 2005 → Apr 23 2005
|Conference||5th SIAM International Conference on Data Mining, SDM 2005|
|City||Newport Beach, CA|
|Period||04/21/05 → 04/23/05|