Package 'skmeans' Title Spherical K-means Clustering
ثبت نشده
چکیده
Description Algorithms to compute spherical k-means partitions. Features several methods, including a genetic and a fixed-point algorithm and an interface to the CLUTO vcluster program.
منابع مشابه
Package ‘ skmeans ’ June 24 , 2010
June 24, 2010 Type Package Title Spherical k-Means Clustering Version 0.1-5 Author Kurt Hornik, Ingo Feinerer, Martin Kober Maintainer Kurt Hornik Description Algorithms to compute spherical k-means partitions. Features several methods, including a genetic and a simple fixed-point algorithm and an interface to the CLUTO vcluster program. License GPL-2 Imports slam (>...
متن کاملTitle Spherical K-means Clustering
October 21, 2009 Type Package Title Spherical k-Means Clustering Version 0.1-2 Author Kurt Hornik, Ingo Feinerer, Martin Kober Maintainer Kurt Hornik Description Algorithms to compute spherical k-means partitions. Features several methods, including a genetic and a simple fixed-point algorithm and an interface to the CLUTO vcluster program. License GPL-2 Imports slam...
متن کاملOn some document clustering algorithms for data mining
We consider the problem of clustering large document sets into disjoint groups or clusters. Our starting point is recent literature on effective clustering algorithms, specifically Principal Direction Divisive Partitioning (PDDP), proposed by Boley in [1] and Spherical k-Means (“S–kmeans” for short) proposed by Dhillon and Moda in [4]. In this paper we study and evaluate the performance of thes...
متن کاملPersistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm
Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...
متن کاملA Hybrid Data Clustering Algorithm Using Modified Krill Herd Algorithm and K-MEANS
Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...
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:
دوره شماره
صفحات -
تاریخ انتشار 2013