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.
Most of the previous research and to the best of our knowledge, previous work has used Apriori type algorithms for mining interesting co-location patterns. However, we embedded GLIMIT @cite_14 as the underlying pattern mining algorithm as already discussed in . To the best of our knowledge, no previous work has used the concept of .
@cite_14
2783760078 2115482638 2064853889 2185538272
Mining patterns from databases is like searching for precious gems which is a gruesome task but still a rewarding one. The frequent patterns are believed to be valuable assets for the researchers that provide them useful information. The frequent and rare pattern mining paradigm is broadly divided into Apriori and FP-Tree-based approaches. Experimental results and performance evaluation available in the literature have established the fact that FP-Tree-based approaches are superior to the Apriori ones on various grounds. This paper explores the various modifications of FP-Tree that were developed to tackle the major pattern mining research challenges. Through this paper, an attempt has been made to review the usefulness and applicability of the most eminent data structure in the domain of pattern mining, the FP-Tree. Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and or long patterns. In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods. Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and or long patterns. In this study, we propose a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a highly condensed, much smaller data structure, which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent pattern mining methods. Local pattern mining methods are fragmented along two dimensions: the pattern syntax, and the data types on which they are applicable. Pattern syntaxes considered in the literature include subgroups, n-sets, itemsets, and many more; common data types include binary, categorical, and real-valued. Recent research on pattern mining in relational databases has shown how the aforementioned pattern syntaxes can be unified in a single framework. However, a unified understanding of how to deal with various data types is lacking, certainly for more complexly structured types such as time of day (which is circular), geographical location, terms from a taxonomy, etc. In this paper, we introduce a generic approach for mining interesting local patterns in (relational) data involving such structured data types as attributes. Importantly, we show how this can be done in a generic manner, by modelling the structure within a set of attribute values as a partial order. We then derive a measure of subjective interestingness of such patterns using Information Theory, and propose an algorithm for effectively enumerating all patterns of this syntax. Through empirical evaluation, we found that (a) the new interestingness derivation is relevant and cannot be approximated using existing tools, (b) the new tool, P-N-RMiner, finds patterns that are substantially more informative, and (c) the new enumeration algorithm is considerably faster.