Maximin Initialization for Cluster Analysis

نویسندگان

  • Richard J. Hathaway
  • James C. Bezdek
  • Jacalyn M. Huband
چکیده

Most iterative clustering algorithms require a good initialization to achieve accurate results. A new initialization procedure for all such algorithms is given that is exact when the data contain compact, separated clusters. Our examples use c-means clustering.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Histogram-Based Method for Effective Initialization of the K-Means Clustering Algorithm

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, this algorithm is highly sensitive to the initial selection of the cluster centers. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have superlinear complexity in the number of data points, which makes them impractical for large data sets. On ...

متن کامل

K-maximin clustering: a maximin correlation approach to partition-based clustering

We propose a new clustering algorithm based upon the maximin correlation analysis (MCA), a learning technique that can minimize the maximum misclassification risk. The proposed algorithm resembles conventional partition clustering algorithms such as k-means in that data objects are partitioned into k disjoint partitions. On the other hand, the proposed approach is unique in that an MCA-based ap...

متن کامل

A new algorithm for choosing initial cluster centers for k-means

The k-means algorithm is widely used in many applications due to its simplicity and fast speed. However, its result is very sensitive to the initialization step: choosing initial cluster centers. Different initialization algorithms may lead to different clustering results and may also affect the convergence of the method. In this paper, we propose a new algorithm for improving the initializatio...

متن کامل

Better Spread and Convergence: Particle Swarm Multiobjective Optimization Using the Maximin Fitness Function

Maximin strategy has its origin in game theory, but it can be adopted for effective multiobjective optimization. This paper proposes a particle swarm multiobjective optimiser, maximinPSO, which uses a fitness function derived from the maximin strategy to determine Pareto-domination. The maximin fitness function has some very desirable properties with regard to multiobjective optimization. One a...

متن کامل

Improving cluster analysis by co-initializations

Many modern clustering methods employ a non-convex objective function and use iterative optimization algorithms to find local minima. Thus initialization of the algorithms is very important. Conventionally the starting guess of the iterations is randomly chosen; however, such a simple initialization often leads to poor clusterings. Here we propose a new method to improve cluster analysis by com...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006