Approximation Bound for K-Means clustering of Binary Data
نویسنده
چکیده
We prove that a p-swap search algorithm for the K-means clustering problem has an approximation bound 3 + 2 p , assuming a binary data set and Euclidean distance. This is tighter than the general bound “ 3 + 2 p ”2 . We also present an example resulting in a cost ratio of 3− . Thus, our bound is almost sharp for the p-swap algorithm.
منابع مشابه
Exact algorithms for solving a bi-level location–allocation problem considering customer preferences
The issue discussed in this paper is a bi-level problem in which two rivals compete in attracting customers and maximizing their profits which means that competitors competing for market share must compete in the centers that are going to be located in the near future. In this paper, a nonlinear model presented in the literature considering customer preferences is linearized. Customer behavior ...
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 کاملModification of the Fast Global K-means Using a Fuzzy Relation with Application in Microarray Data Analysis
Recognizing genes with distinctive expression levels can help in prevention, diagnosis and treatment of the diseases at the genomic level. In this paper, fast Global k-means (fast GKM) is developed for clustering the gene expression datasets. Fast GKM is a significant improvement of the k-means clustering method. It is an incremental clustering method which starts with one cluster. Iteratively ...
متن کاملFUZZY K-NEAREST NEIGHBOR METHOD TO CLASSIFY DATA IN A CLOSED AREA
Clustering of objects is an important area of research and application in variety of fields. In this paper we present a good technique for data clustering and application of this Technique for data clustering in a closed area. We compare this method with K-nearest neighbor and K-means.
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 2009