Dissimilarity Clustering by Hierarchical Multi-Level Refinement
نویسندگان
چکیده
We introduce in this paper a new way of optimizing the natural extension of the quantization error using in k-means clustering to dissimilarity data. The proposed method is based on hierarchical clustering analysis combined with multi-level heuristic refinement. The method is computationally efficient and achieves better quantization errors than the relational k-means.
منابع مشابه
MLCA: A Multi-Level Clustering Algorithm for Routing in Wireless Sensor Networks
Energy constraint is the biggest challenge in wireless sensor networks because the power supply of each sensor node is a battery that is not rechargeable or replaceable due to the applications of these networks. One of the successful methods for saving energy in these networks is clustering. It has caused that cluster-based routing algorithms are successful routing algorithm for these networks....
متن کاملHierarchical image segmentation by multi-dimensional clustering and orientation-adaptive boundary refinement
In this paper we present a new multi-dimensional segmentation algorithm. We propose an orientation-adaptive boundary estimation process, embedded in a multiresolution pyramidal structure, that allows the use of different clustering procedures without spatial connectivity constraints. The presence of noise in the feature space, mainly produced by modeling errors, causes a class-overlap which can...
متن کاملOn Data-Independent Properties for Density-Based Dissimilarity Measures in Hybrid Clustering
Hybrid clustering combines partitional and hierarchical clustering for computational effectiveness and versatility in cluster shape. In such clustering, a dissimilarity measure plays a crucial role in the hierarchical merging. The dissimilarity measure has great impact on the final clustering, and data-independent properties are needed to choose the right dissimilarity measure for the problem a...
متن کاملCommon Dissimilarity Measures are Inappropriate for Time Series Clustering
Clustering algorithms have been actively used to identify similar time series, providing a better understanding of data. However, common clustering dissimilarity measures disregard time series correlations, yielding poor results. In this paper, we introduce a dissimilarity measure based on series partial autocorrelations. Experiments compare hierarchical clustering algorithms using the common d...
متن کاملA Relative Approach to Hierarchical Clustering
This paper presents a new approach to agglomerative hierarchical clustering. Classical hierarchical clustering algorithms are based on metrics which only consider the absolute distance between two clusters, merging the pair of clusters with highest absolute similarity. We propose a relative dissimilarity measure, which considers not only the distance between a pair of clusters, but also how dis...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- CoRR
دوره abs/1204.6509 شماره
صفحات -
تاریخ انتشار 2012