FAST K-MEANS COLOR IMAGE CLUSTERING WITH NORMALIZED DISTANCE VALUES
نویسندگان
چکیده
منابع مشابه
Fuzzy K-means clustering with missing values
Fuzzy K-means clustering algorithm is a popular approach for exploring the structure of a set of patterns, especially when the clusters are overlapping or fuzzy. However, the fuzzy K-means clustering algorithm cannot be applied when the real-life data contain missing values. In many cases, the number of patterns with missing values is so large that if these patterns are removed, then sufficient...
متن کاملNormalized k-means clustering of hyper-rectangles
Interval variables can be measured on very different scales. We first remind a general methodology used for measuring the dispersion of a variable from an optimal center and we define two measures of dispersions associated to two optimal ”centers” for interval variables. Then we study the relations between the standardization of a data table and the use in clustering of a normalized distance. F...
متن کاملColor Image Segmentation Using a Spatial K-Means Clustering Algorithm
This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In additi...
متن کاملFast k-means algorithm clustering
k-means has recently been recognized as one of the best algorithms for clustering unsupervised data. Since k-means depends mainly on distance calculation between all data points and the centers, the time cost will be high when the size of the dataset is large (for example more than 500millions of points). We propose a two stage algorithm to reduce the time cost of distance calculation for huge ...
متن کاملLightning fast asynchronous distributed k-means clustering
One of the most fundamental data processing approach is the clustering. This is even true in distributed architectures. Here, we focus on the problem of designing efficient and fast K-Means approaches which work in fully distributed, asynchronous networks without any central control. We assume that the network has a huge number of computational units (even orders of magnitude more than the numb...
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: Selcuk University Journal of Engineering ,Science and Technology
سال: 2018
ISSN: 2147-9364
DOI: 10.15317/scitech.2018.124