Random search with k-prototypes algorithm for clustering mixed datasets
نویسندگان
چکیده
BY DUC-TRUONG PHAM1,2, MARIA M. SUAREZ-ALVAREZ1,* AND YURIY I. PROSTOV3 1Manufacturing Engineering Centre, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK 2Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia 3Department of Higher Mathematics, Moscow Institute of Radio Engineering, Electronics and Automation—Technical University, 78 Vernadskogo pr., Moscow 117454, Russia
منابع مشابه
A 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 کاملTabu-KM: A Hybrid Clustering Algorithm Based on Tabu Search Approach
The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the ...
متن کاملScalable Image Annotation by Summarizing Training Samples into Labeled Prototypes
By increasing the number of images, it is essential to provide fast search methods and intelligent filtering of images. To handle images in large datasets, some relevant tags are assigned to each image to for describing its content. Automatic Image Annotation (AIA) aims to automatically assign a group of keywords to an image based on visual content of the image. AIA frameworks have two main sta...
متن کاملAn improved k-prototypes clustering algorithm for mixed numeric and categorical data
Data objects with mixed numeric and categorical attributes are commonly encountered in real world. The k-prototypes algorithm is one of the principal algorithms for clustering this type of data objects. In this paper, we propose an improved k-prototypes algorithm to cluster mixed data. In our method, we first introduce the concept of the distribution centroid for representing the prototype of c...
متن کاملPrototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the term “prototypes” refers to the reference instances used in a nearest neighbor computation — the instances with respect to which similarity is assessed in order to assign a class to a new data item. Both algorithms rely ...
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 2011