Abstract—Web usage mining is an interesting application of data mining which provides insight into customer behaviour on the Internet. An important technique to discover user access and navigation trails is based on sequential patterns mining. One of the key challenges for web access patterns mining is tackling the problem of mining richly structured patterns. This paper proposes a novel model ...

This thesis describes research work undertaken in the field of graph-based knowledge discovery (or graph mining). The objective of the research is to investigate the benefits that the concept of weighted frequent subgraph mining can offer in the context of the graph model based classification. Weighted subgraphs are graphs where some of the vertexes/edges are considered to be more significant t...

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During the past decade, we have witnessed an explosive growth in our capabilities to both generate and collect data. Various data mining techniques have been proposed and widely employed to discover valid, novel and potentially useful patterns in these data. Data mining involves the discovery of patterns, associations, changes, anomalies, and statistically significant structures and events in h...

In the first step of drug discovery process, a large number of lead compounds are found by high throughput screening. To identify physicochemical properties of the lead compounds, SAR and QSAR analyses are commonly applied (Gasteiger & Engel, 2003). In machine learning terminology, SAR is understood as a classification task where a chemical compound is given as an input, and the learning machin...

Recently, methods for mining graph sequences have attracted considerable interest in datamining research. A graph sequence is a data structure used to represent changing networks. The aim of graph sequence mining is to enumerate common changing patterns appearing more frequently than a given threshold in graph sequences. Dependency analysis is recognized as a basic process in natural language p...

In this paper, we propose a new mining task: mining top-k frequent closed graph patterns without minimum support. Most previous frequent graph pattern mining works require the specification of a minimum support threshold to perform the mining. However it is difficult for users to set a suitable value sometimes. We develop an efficient algorithm, called TGP, to mine patterns without minimum supp...

The Thesis aims at in depth study of Graph based approach to find Association Rule Mining and Association Rule Mining without pre-assign weights and its implementation. In the past, several authors have proposed various association rule mining algorithms with and without pre-assigned weights. The Thesis consists of two parts. Part two consists of graph based association rule mining for primitiv...

Frequent subgraph mining (FSG) has always been an important issue in data mining. Several frequent subgraph mining methods have been developed for mining graph data. However, most of these are main memory algorithms in which scalability is a bigger issue. A few algorithms have opted for a relational approach that stores the graph data in relational tables. However, relational databases have the...