Clustering for Probability Density Functions by New k-Medoids Method
نویسندگان
چکیده
منابع مشابه
Document Clustering using K-Medoids
People are always in search of matters for which they are prone to use internet, but again it has huge assemblage of data due to which it becomes difficult for the reader to get the most accurate data. To make it easier for people to gather accurate data, similar information has to be clustered at one place. There are many algorithms used for clustering of relevant information in one platform. ...
متن کاملUnsupervised Clustering by k-medoids for Video Summarization
In this paper, we propose a video summarization algorithm by multiple extractions of key frames in each shot. This algorithm is based on the k partition algorithms. We choose the ones based on k-medoid clustering methods so as to find the best representative object for each partitions. In order to find the number of partition (i.e. the number of representative frames of each shot), we introduce...
متن کاملClustering of Amino Acid Sequences Based on K-Medoids Method
【Abstract】We describe a new approach to clustering of amino acid sequences using K-Medoids Method. This method combines K-Medoids method, Dynamic Programming and other new theories in Biology. Experiments have proved that our method can get satisfying results. We believe that the method we proposed in this paper is a powerful and flexible tool for clustering of amino acid sequences. 【Keywords】C...
متن کاملA K-means-like Algorithm for K-medoids Clustering
Clustering analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting initial medoids. The proposed algorithm calculates the distance matrix once and uses it for finding new medoids at every i...
متن کاملColour image segmentation using K – Medoids Clustering
K – medoids clustering is used as a tool for clustering color space based on the distance criterion. This paper presents a color image segmentation method which divides colour space into clusters. Through this paper, using various colour images, we will try to prove that K – Medoids converges to approximate the optimal solution based on this criteria theoretically as well as experimentally. Her...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Scientific Programming
سال: 2018
ISSN: 1058-9244,1875-919X
DOI: 10.1155/2018/2764016