1. A study on graph mining algorithms to discover frequent subgraph patterns from exact graph data and uncertain graph database
Authors : Saroj A. Shambharkar, D.anantha Reddy, Mohammed Jabeed Rihaz
Pages : 246-250
DOI : http://dx.doi.org/10.21172/1.93.41
Keywords : Keywords – Expected support, Graph mining, spatial motifs,supervised learning,uncertain data,unsupervised learning. Abstract :Abstract- For knowledge discovery from complex objects we require mining algorithms,they extract frequent subgraphs from graph database,required to know the relationship between the object. In this paper the different approaches/algorithms,techniques and methods for finding the frequent graph patterns from the given dataset is specified. The dataset can be certain graph data or Uncertain graph data.The paper discusses about the approaches,techniques and methods used to find the frequent graph patterns on protein structure and genes. There are also several algorithms for finding the frequent subgraph. One of the novel frequent subgraph mining algorithm is used to solve one of the important problem in Bioinformatics is finding recurring residue packing patterns and spatial motifs. There are different frequent subgraph mining algorithms such as gSpan, FFSM,SPIN,SUBDUE .This paper presented the information about the algorithms for frequent subgraph mining algorithms,techniques of graph mining,domain where graph mining is used and also the creation of subgraphs from the graph database. In this paper it is also mentioned that we can use mining algorithms to extract the frequent subgraph patterns from certain graph data as well as we can extract frequent subgraph patterns from uncertain graph data / databases. To discover the frequent subgraph patterns from the uncertain graph database is a very challenging job as uncertainties may occur due imprecision data.
Citing this Journal Article :Saroj A. Shambharkar, D.anantha Reddy, Mohammed Jabeed Rihaz, "A study on graph mining algorithms to discover frequent subgraph patterns from exact graph data and uncertain graph database", Volume 9 Issue 3 - January 2018, 246-250
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