mining projected clusters
Mining projected clusters in high-dimensional spaces
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ABSTRACT Clustering high-dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full-dimensional
Fast algorithms for projected clustering
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weakness of this approach is that in typical high dimensional data mining applications different discuss a generalization of the clustering problem, referred to as the projected clustering problem in which the subsets of dimensions selected are specific to the clusters themselves.
A survey of clustering data mining techniques
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Because the mixture model has a clear probabilistic foundation, the deter- mination of the most suitable number of clusters k becomes more tractable. From a data mining perspective, excessive parameter setting can cause over- fitting, while from a probabilistic perspective, the
Data mining and personalization technologies
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Various data mining techniques can be used to im- prove recommendation systems. with a subset C of data points such that the points in C are closely clustered in the projected subspace of dimen- sions D. In Figure 1, two clusters exist in two di erent projected subspaces.
A framework for projected clustering of high dimensional data streams
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Of course, these subsets of dimensions may vary over the different clusters. Such clusters are referred to as projected clusters [2]. 852 Page 2. In the context of a data stream, the problem of find- ing projected clusters becomes even more challenging.
P3C: A robust projected clustering algorithm
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similarity in full dimensional space. In this paper, we propose an algorithm for mining pro- jected clusters, called P3C (Projected Clustering via Cluster Cores) with the following properties. • P3C effectively discovers the projected
Cluster analysis for data mining and system identification
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but it can be used for visualiza- tion, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system If this data mass is projected into 6.3 billion inhabitants of the Earth, then it roughly means that
A framework for finding projected clusters in high dimensional spaces
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The weakness of this approach is that in typical high dimensional data mining applications di introduce a generalization of the clustering problem, referred to as the projected clustering problem in which the subsets of dimensions selected are speci c to the clusters themselves.
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
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ABSTRACT In this paper we introduce a novel algorithm called tri- Cluster, for mining coherent clusters in three-dimensional (3D) gene expression datasets. However, our our definition allows for the mining of shifting clusters as well, as indicated by the lemma below.
Op-cluster: Clustering by tendency in high dimensional space
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ones by constructing projected databases. In our paper, we are facing a similar but more compli- cated problem than sequential pattern mining. Rows in ma- trix will be treated as a sequence to find sequential patterns. However, in order to finally determine OP-Cluster, the ID
Data mining techniques for personalization
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For such cases many data mining techniques such as associations, clustering, and categorization Finding Generalized Projected Clusters in High Dimensional Spaces. Proceed- ings of the ACM
SCHISM: A new approach for interesting subspace mining
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Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04) . Projected Clustering Aggarwal [2, 3] uses projective clustering to partition the dataset into clusters occurring in possibly different subsets of
Using emerging pattern based projected clustering and gene expression data for cancer detection
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Traditional clustering and pattern mining algorithm are either inadequate to handle high dimensional gene We proposed emerging pattern based projected clustering (EPPC) approaches to cope with the Previous result shows that easy understandable clusters are obtained.
On discovery of extremely low-dimensional clusters using semi-supervised projected clustering
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The same kind of low dimensional clusters could also ex- ist in datasets from various domains such as computer vi- sion [15], e-commerce [19], text mining and nutrition value analysis [24]. The projected clustering problem is defined for such a scenario.
Towards effective and interpretable data mining by visual interaction
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Extensions to other potential data mining problems are discussed in section 4. The conclusion and summary is discussed in section 5. Such distributions create clusters in lower dimensional pro- jections and are referred to as projected clusters.
Robust projected clustering
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counterparts. Contributions and outline of the paper. In this paper, we propose an algorithm for mining projected clusters, called P3C (Projected Clustering via Cluster Cores) with the following properties.
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