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GCG: Mining Maximal Complete Graph Patterns from Large Spatial Data
Databases · Submitted 13 Dec 2013
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Recent research on pattern discovery has progressed from mining frequent patterns and sequences to mining structured patterns, such as trees and graphs. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Graphs as general data structure can model complex relations among data with wide applications in web exploration and social networks. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | However, the process of mining large graph patterns is a challenge due to the existence of large number of subgraphs. | background 0.87 | 0.06 | 0.03 | asserted 1.00 |
| s3 | In this paper, we aim to mine only frequent complete graph patterns. | contribution 0.98 | 0.42 | 0.13 | asserted 0.91 |
| s4 | A graph g in a database is complete if every pair of distinct vertices is connected by a unique edge. | background 0.88 | 0.07 | 0.03 | asserted 1.00 |
| s5 | 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. | contribution 0.72 | 0.85 | 0.16 | asserted 1.00 |
| s6 | Using a divide and conquer strategy, GCG shows high efficiency especially in the presence of large number of patterns. | result 0.52 | 0.78 | 0.21 | asserted 1.00 |
| s7 | In this paper, we describe GCG that can mine not only simple co-location spatial patterns but also complex ones. | contribution 0.97 | 0.76 | 0.16 | asserted 0.99 |
| s8 | 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. | contribution 0.79 | 0.61 | 0.35 | asserted 0.97 |
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Original 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.
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