Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Fast visualization methods for comparing dynamics: a case study in combustion
Proceedings of the conference on Visualization '00
Principles of data mining
Machine Learning
Information Visualization in Data Mining and Knowledge Discovery
Information Visualization in Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Using text mining and link analysis for software mining
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Visual web mining for website evaluation
Journal of Web Engineering
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This paper presents a method for visualization of large graphs in a two-dimensional space, such as a collection of Web pages. The main contribution here is in the representation change to enable better handling of the data. The idea of the method consists from three major steps: (1) First, we transform a graph into a sparse matrix, where for each vertex in the graph there is one sparse vector in the matrix. Sparse vectors have non-zero components for the vertices that are close to the vertex represented by the vector. (2) Next, we perform hierarchical clustering (eg., hierarchical K-Means) on the set of sparse vectors, resulting in the hierarchy of clusters. (3) In the last step, we map hierarchy of clusters into a two-dimensional space in the way that more similar clusters appear closely on the picture. The effect of the whole procedure is that we assign unique X and Y coordinates to each vertex, in a way those vertices or groups of vertices on several levels of hierarchy that are stronger connected in a graph are place closer in the picture. The method is particular useful for power distributed graphs. We show applications of the method on real-world examples of visualization of institution collaboration graph and cross-sell recommendation graph.