Weighted Approach to Projective Clustering
نویسندگان
چکیده
k-means is the basic method applied in many data clustering problems. As is known, its natural modification can be applied to projection clustering by changing the cost function from the squared-distance from the point to the squared distance from the affine subspace. However, to apply thus approach we need the beforehand knowledge of the dimension. In this paper we show how to modify this approach to allow greater flexibility by using the weights over respective range of subspaces.
منابع مشابه
Bilateral Weighted Fuzzy C-Means Clustering
Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some k...
متن کاملA Framework for Optimal Attribute Evaluation and Selection in Hesitant Fuzzy Environment Based on Enhanced Ordered Weighted Entropy Approach for Medical Dataset
Background: In this paper, a generic hesitant fuzzy set (HFS) model for clustering various ECG beats according to weights of attributes is proposed. A comprehensive review of the electrocardiogram signal classification and segmentation methodologies indicates that algorithms which are able to effectively handle the nonstationary and uncertainty of the signals should be used for ECG analysis. Ex...
متن کاملLinear Time Algorithm for Projective Clustering
Projective clustering is a problem with both theoretical and practical importance and has received a great deal of attentions in recent years. Given a set of points P in R space, projective clustering is to find a set F of k lower dimensional j-flats so that the average distance (or squared distance) from points in P to their closest flats is minimized. Existing approaches for this problem are ...
متن کاملOn the Sensitivity of Shape Fitting Problems
In this article, we study shape fitting problems, -coresets, and total sensitivity. We focus on the (j, k)-projective clustering problems, including k-median/k-means, k-line clustering, j-subspace approximation, and the integer (j, k)-projective clustering problem. We derive upper bounds of total sensitivities for these problems, and obtain -coresets using these upper bounds. Using a dimension-...
متن کاملWeighted Ensemble Clustering for Increasing the Accuracy of the Final Clustering
Clustering algorithms are highly dependent on different factors such as the number of clusters, the specific clustering algorithm, and the used distance measure. Inspired from ensemble classification, one approach to reduce the effect of these factors on the final clustering is ensemble clustering. Since weighting the base classifiers has been a successful idea in ensemble classification, in th...
متن کامل