Abstract
Graphs are one of the popular models for effective representation of complex structured huge data and the similarity search for graphs has become a fundamental research problem in Graph Mining. In this paper initially, the preliminary graph related basic theorems are brushed and showcased on with various research sub domains such as Graph Classification, Graph Searching, Graph Indexing, and Graph Clustering. These are discussed with few of the most dominant algorithms in their respective sub domains. Finally a model is proposed along with various algorithms with their future projection.
Highlights
The primary goal of data mining is to extract statistically significant and useful knowledge from data [1][2][3] which may be in any of the forms like image, text, links, vectors, tables and so on
We show case the various sub domains in the field of graph mining and a model to index, update and upgrade without performance degradation
Graph classification [12], graph indexing [10][11], and graph clustering [13][18], sub graphs patterns as features are some of the major key areas of research in Graph Mining
Summary
The primary goal of data mining is to extract statistically significant and useful knowledge from data [1][2][3] which may be in any of the forms like image, text, links, vectors, tables and so on. Graph classification [12], graph indexing [10][11], and graph clustering [13][18], sub graphs patterns as features are some of the major key areas of research in Graph Mining.
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