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It is well known that clustering is an unsupervised machine learning technique. However, most of the clustering methods need setting several parameters such as number of clusters, shape of clusters, or other user- or problem-specific parameters and thresholds. In this paper, we propose a new clustering approach which is fully autonomous, in the sense that it does not require parameters to be pre-defined...
This paper provides a method of mathematical representation of the traffic flow of network states. Anomalous behavior in this model is represented as a point, not grouped in clusters allocated by the "alpha-stream" process.
Molecular dynamics is a widely used simulation technique to investigate material properties and structural changes under external forces. The availability of more powerful clusters and algorithms continues to increase the spatial and temporal extents of the simulation domain. This poses a particular challenge for the visualization of the underlying processes which might consist of millions of particles...
Clustering is the process of organizing a collection of patterns into groups based on their similarities. Fuzzy clustering techniques aim at finding groups to which every object in the database belongs to some membership degree. This paper presents a new algorithm for clustering symbolic data based on ckMeans algorithm. This new algorithm allows the data entry and the membership degree to be intervals...
We present a novel visualization methodology for graphs and high-dimensional data which combines the neighborhood preservation characteristics of the minimum spanning trees, with the grouping properties of dendrograms. We call the method `minimum spanning dendrogram'. We highlight the ability of the mapping to accurately capture both neighborhood and cluster structures. The technique accommodates...
Subspace clusters represent useful information in high-dimensional data. However, mining significant subspace clusters in continuous-valued 3D data such as stock-financial ratio-year data, or gene-sample-time data, is difficult. Firstly, typical metrics either find subspaces with very few objects, or they find too many insignificant subspaces - those which exist by chance. Besides, typical 3D subspace...
In this paper, we present our continuous research on similarity search problems. Previously we proposed PanKNN[18] which is a novel technique that explores the meaning of K nearest neighbors from a new perspective, redefines the distances between data points and a given query point Q, and efficiently and effectively selects data points which are closest to Q. It can be applied in various data mining...
Many internet-based applications such as social networking websites, online viral marketing, and recommendation network based applications, use social network analysis to improve performance in terms of user-specific information dissemination. The notion of community in a social network is a key concept in such analyses and there has been significant work recently in identifying communities within...
This paper presents a new index for measuring interval distances and its related metric. The proposed index and metric are both based on the Hausdorff distance which can be used for clustering uncertain interval data. Then using the new metric, a clustering method is introduced for clustering of intervals. Finally, some experiments are provided to validate the method. Results show that the method...
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