منابع مشابه
Support Vector Clustering
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. This sphere, when mapped back to data space, can separate into several components, each enclosing a separate cluster of points. We present a simple algorithm for identifying...
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In this document we present the results of some preliminary experiments using Support Vector Clustering (SVC). The experiments were conducted both over synthetic data sets and over real-world data sets taken from UCI Repository of Machine Learning Database.
متن کاملCone Cluster Labeling for Support Vector Clustering
Clustering forms natural groupings of data points that maximize intra-cluster similarity and minimize intercluster similarity. Support Vector Clustering (SVC) is a clustering algorithm that can handle arbitrary cluster shapes. One of the major SVC challenges is a cluster labeling performance bottleneck. We propose a novel cluster labeling algorithm that relies on approximate coverings both in f...
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7 This study presents two new clustering algorithms for partition of data sam8 ples for the Support Vector Machine (SVM) based hierarchical classification. 9 A divisive (top-down) approach is considered in which a set of classes is 10 automatically separated into two smaller groups at each node of the hierar11 chy. The first algorithm splits the data samples based on a variation of the 12 Norma...
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We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to define a sphere enclosing them. The boundary of the sphere forms in data space a set of closed contours containing the data. As the kernel parameter is varied these contours fit the data more tightly and splitting of co...
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ژورنال
عنوان ژورنال: Scholarpedia
سال: 2008
ISSN: 1941-6016
DOI: 10.4249/scholarpedia.5187