The existing studies are mostly focused on the multiple graphs scenario. With some modifications, the mining methodology can be extended to the single graph scenario [30]. Washio and Motoda [56] conducted a survey on graph-based data mining. Holder et al. [21] proposed SUBDUE to do subgraph pattern discovery based on minimum description length and background knowledge. The most popular graph pa...

This paper represents a technique for recovering the Software Architecture based on Graph Pattern Matching by the help of mining techniques. Generally Software Architecture is represented in terms of graphs with set of vertices and edges. Finding the frequent data sets is the major step in the software architecture recovery. Many algorithms are proposed in this context, for example Apriori base...

Web Usage Mining refers to the discovery of interesting information from user navigational behavior as stored in web access logs. While extracting simple information from web logs is easy, mining complex structural information is very challenging. Data cleaning and preparation constitute a very significant effort before mining can even be applied. We propose two new XML applications, XGMML and ...

Frequent subgraph mining is an active research topic in the data mining community. A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets,...

SUMMARY The NetAffx Gene Ontology (GO) Mining Tool is a web-based, interactive tool that permits traversal of the GO graph in the context of microarray data. It accepts a list of Affymetrix probe sets and renders a GO graph as a heat map colored according to significance measurements. The rendered graph is interactive, with nodes linked to public web sites and to lists of the relevant probe set...

In drug discovery, one key step is to predict chemical activities of drug candidates such as toxicity, mutagenicity and solubility and thereby reduce the number of chemical compounds screened by actual experimental means. Such computational prediction is called “virtual screening”. Drug candidates are small molecules which are represented as attributed graphs where atoms and bonds correspond to...

A lot of real world problems can be modeled as traversals on graph, and mining from such traversals has been found useful in several applications. However, previous works considered only traversals on unweighted graph. This paper generalizes this to the case where vertices of graph are given weights to reflect their importance. Under such weight settings, traditional mining algorithms can not b...

Mining Human Interaction flow in meetings or general representation of any interaction face to face to meetings is useful to identify the person reaction in dissimilar situation. Activities represent the natural history of the individual and mining methods help to analyze how person delivers their opinion in different ways. Meeting interactions are categorized as propose, comment, acknowledgeme...

Mining for frequent subgraphs in a graph database has become a popular topic in the last years. Algorithms to solve this problem are used in chemoinformatics to find common molecular fragments in a database of molecules represented as two-dimensional graphs. However, the search process in arbitrary graph structures includes costly graph and subgraph isomorphism tests. In our ParMol package we h...

Recent advances in satellite tracking technologies can provide huge amount of data for biologists to understand continuous long movement patterns of wild bird species. In particular, highly correlated habitat areas are of great biological interests. Biologists can use this information to strive potential ways for controlling highly pathogenic avian influenza. We convert these biological problem...