DocumentCode :
638301
Title :
GCG: Mining maximal complete graph patterns from large spatial data
Author :
Al-Naymat, Ghazi
Author_Institution :
Coll. of Comput. Sci. & Inf. Technol., Univ. of Dammam, Dammam, Saudi Arabia
fYear :
2013
fDate :
27-30 May 2013
Firstpage :
1
Lastpage :
8
Abstract :
Recent research on pattern discovery has progressed from mining frequent patterns and sequences to mining structured patterns, such as trees and graphs. Graphs as general data structure can model complex relations among data with wide applications in web exploration and social networks. However, the process of mining large graph patterns is a challenge due to the existence of large number of subgraphs. In this paper, we aim to mine only frequent complete graph patterns. A graph g in a database is complete if every pair of distinct vertices is connected by a unique edge. Grid Complete Graph (GCG) is a mining algorithm developed to explore interesting pruning techniques to extract maximal complete graphs from large spatial dataset existing in Sloan Digital Sky Survey (SDSS) data. Using a divide and conquer strategy, GCG shows high efficiency especially in the presence of large number of patterns. In this paper, we describe GCG that can mine not only simple co-location spatial patterns but also complex ones. To the best of our knowledge, this is the first algorithm used to exploit the extraction of maximal complete graphs in the process of mining complex co-location patterns in large spatial dataset.
Keywords :
data mining; divide and conquer methods; information retrieval; network theory (graphs); social networking (online); GCG; SDSS; Sloan Digital Sky Survey; Web exploration; connected graph; divide and conquer strategy; frequent pattern mining; grid complete graph; maximal complete graph extraction; maximal complete graph pattern mining; pattern discovery; pruning technique; social network; spatial data; structured pattern mining; subgraph; Catalogs; Data mining; Earth; Extraterrestrial measurements; Itemsets; Spatial databases; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Systems and Applications (AICCSA), 2013 ACS International Conference on
Conference_Location :
Ifrane
ISSN :
2161-5322
Type :
conf
DOI :
10.1109/AICCSA.2013.6616417
Filename :
6616417
Link To Document :
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