Topic
Centroid
About: Centroid is a research topic. Over the lifetime, 4110 publications have been published within this topic receiving 53637 citations. The topic is also known as: barycenter (geometry) & geometric center of a plane figure.
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06 Mar 2010
TL;DR: Simulations demonstrate that the clique clustering technique out-performs k-means clustering and is nearly as effective as the 1-target likelihood peak methods at a fraction of the computational cost.
Abstract: This works presents the maximum likelihood localization (ML) algorithm for multi-target localization using proximity-based sensor networks. Proximity sensors simply report a single binary value indicating whether or not a target is near. The ML approach requires a hill climbing algorithm to find the peak, and its ability to find the global peak is determined by the initial estimates for the target locations. This paper investigates three methods to initialize the ML algorithm: 1) centroid of k-means clustering, 2) centroid of clique clustering, and 3) peak in the 1-target likelihood surface. To provide a performance bound for the initialization methods, the paper also considers the ground truth target positions as initial estimates. Simulations compare the ability of these methods to resolve and localize two targets. The simulations demonstrate that the clique clustering technique out-performs k-means clustering and is nearly as effective as the 1-target likelihood peak methods at a fraction of the computational cost.
22 citations
26 Sep 1999
TL;DR: A novel method for face recognition using profile images based on the representation of the original and morphological derived profile shapes is presented, using information from profile outline that bounds the face and the hair.
Abstract: A novel method for face recognition using profile images based on the representation of the original and morphological derived profile shapes is presented in this paper. The goal of our approach is to use information from profile outline that bounds the face and the hair. A grey-level profile image is thresholded to produce a binary image, representing the face region. After normalising the area and orientation of this shape using basic morphological operations, dilation and erosion, we simulate hair growth and haircut and produce two new profile silhouettes. From this three profile shape feature vectors are obtained using distances between outline curve points and shape centroid. After normalising the vector components, the Euclidean distance measure is used for measuring the similarity of the feature vectors derived from different profiles. Experiments were performed on profile images of thirty persons. Experimental results including recognition rates are presented and discussed.
22 citations
TL;DR: A new cluster validity index, Saraswat-and-Mittal index, has been proposed in this article for hyperellipsoid or hyperspherical shape close clusters with distant centroids, generated by fuzzy c-means, and validated against ten state-of-the-art cluster validity indices.
Abstract: Determining the correct number of clusters is essential for efficient clustering and cluster validity indices are widely used for the same. Generally, the effectiveness of a cluster validity index relies on two factors: first, separation, defined by the distance between a pair of cluster centroids or a pair of data points belonging to different clusters and second, compactness, which is determined in terms of the distance between a data point and a centroid or between a pair of data points belonging to the same cluster. However, the existing cluster validity indices for centroid-based clustering are unreliable when the clusters are too close, but corresponding centroids are distant. To mitigate this, a new cluster validity index, Saraswat-and-Mittal index, has been proposed in this article for hyperellipsoid or hyperspherical shape close clusters with distant centroids, generated by fuzzy c-means. The proposed index computes compactness in terms of the distance between data points and corresponding centroids, whereas the distance between data points of disjoint clusters defines separation. These parameters benefit the proposed index in the analysis of close clusters with distinct centroids efficiently. The performance of the proposed index is validated against ten state-of-the-art cluster validity indices on artificial, UCI, and image datasets, clustered by the fuzzy c-means.
22 citations
01 Jan 2013
TL;DR: This paper introduces an efficient method to start the k -Means with good initial centroids, which are useful for better clustering.
Abstract: Clustering is one of the important data mining techniques. k-Means (1) is one of the most important algorithm for Clustering. Traditional k-Means algorithm selects initial centroids randomly and in k-Means a lgorithm result of clustering highly depends on selection of initial centroids. k-Means algorithm is sensitive to initia l centroids so proper selection of initial centroids is necessa ry. This paper introduces an efficient method to start the k -Means with good initial centroids. Good initial centroids are useful for better clustering.
22 citations
TL;DR: A novel method for ranking fuzzy numbers that integrates the centroid point and the spread approaches and overcomes the limitations and weaknesses of most existing methods is proposed.
Abstract: Centroid and spread are commonly used approaches in ranking fuzzy numbers. Some experts rank fuzzy numbers using centroid or spread alone while others tend to integrate them together. Although a lot of methods for ranking fuzzy numbers that are related to both approaches have been presented, there are still limitations whereby the ranking obtained is inconsistent with human intuition. This paper proposes a novel method for ranking fuzzy numbers that integrates the centroid point and the spread approaches and overcomes the limitations and weaknesses of most existing methods. Proves and justifications with regard to the proposed ranking method are also presented.
22 citations