An Incremental DC Algorithm for the Minimum Sum-of-Squares Clustering
نویسنده
چکیده مقاله:
Here, an algorithm is presented for solving the minimum sum-of-squares clustering problems using their difference of convex representations. The proposed algorithm is based on an incremental approach and applies the well known DC algorithm at each iteration. The proposed algorithm is tested and compared with other clustering algorithms using large real world data sets.
منابع مشابه
An improved column generation algorithm for minimum sum-of-squares clustering
Given a set of entities associated with points in Euclidean space, minimum sum-of-squares clustering (MSSC) consist in partitioning this set into clusters such that the sum of squared distances from each point to the centroid of its cluster is minimized. A column generation algorithm for MSSC was given in du Merle et al. [15]. The bottleneck of that algorithm is resolution of the auxiliary prob...
متن کاملOn the Complexity of Minimum Sum-of-Squares Clustering
To the best of our knowledge, the complexity of minimum sum-of-squares clustering is unknown. Yet, it has often been stated that this problem is NP-hard. We examine causes for such confusion and in the process show that a recent proof of Drineas et al. in Machine Learning 56, 9–33, 2004 is not valid and unlikely to be salvaged.
متن کاملModified global k-means algorithm for minimum sum-of-squares clustering problems
k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and inefficient for solving clustering problems in large data sets. Recently, a new version of the k-means algorithm, the global k-means algorithm has been developed. It is an incremental algorithm that dynamically adds one cluster center at a time and us...
متن کاملA Branch-and-Cut SDP-Based Algorithm for Minimum Sum-of-Squares Clustering
Minimum sum-of-squares clustering (MSSC) consists in partitioning a given set of n points into k clusters in order to minimize the sum of squared distances from the points to the centroid of their cluster. Recently, Peng and Xia (StudFuzz, 2005) established the equivalence between 0-1 semidefinite programming (SDP) and MSSC. In this paper, we propose a branch-and-cut algorithm for the underlyin...
متن کاملEvaluating a branch-and-bound RLT-based algorithm for minimum sum-of-squares clustering
Minimum sum-of-squares clustering consists in partitioning a given set of n points into c clusters in order to minimize the sum of squared distances from the points to the centroid of their cluster. Recently, Sherali and Desai (JOGO, 2005) proposed a reformulationlinearization based branch-and-bound algorithm for this problem, claiming to solve instances with up to 1000 points. In this paper, t...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ذخیره در منابع من قبلا به منابع من ذحیره شده{@ msg_add @}
عنوان ژورنال
دوره 5 شماره None
صفحات 1- 14
تاریخ انتشار 2014-05
با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023