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In some applications, the whole structure of the target data can be represented naturally in "multi-structured graphs" that are complex graphs whose vertices consist of aset of structured data such as itemsets, sequences and so on. To catch the strong affinity relationship in multi-structured graphs, in this paper, we propose an algorithm named HFMG to discover novel and meaningful frequent...
Structured data is becoming increasingly abundant in many application domains recently. In this paper, as one of the correlation mining, we propose new data mining problems of finding frequent and correlated pairs of patterns in structured databases. First, we consider the problem of finding all frequent and correlated pattern pairs in two dimensional structured databases. Then, two kinds of top-k...
This paper presents a novel approach to feature construction for structured data in order to enhance graph prediction classification performance. To this end we combine graph mining techniques with graph kernel based classifiers. The main idea is to employ efficient mining techniques to extract a set of patterns correlated with the target concept and use these, or a selected subset of these, to annotate...
We propose a dynamic graph-based relational mining approach using graph-rewriting rules to learns patterns in networks that structurally change over time. A dynamic graph containing a sequence of graphs over time represents dynamic properties as well as structural properties of the network. Our approach discovers graph-rewriting rules, which describe the structural transformations between two sequential...
We present Graphite, a system that allows the user to visually construct a query pattern, finds both its exact and approximate matching subgraphs in large attributed graphs, and visualizes the matches. For example, in a social network where a person's occupation is an attribute, the user can draw a 'star' query for "finding a CEO who has interacted with a Secretary, a Manager, and an Accountant,...
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