Euclidean, Manhattan and Minkowski Distance Methods For Clustering Algorithms
نویسندگان
چکیده
منابع مشابه
Fuzzy clustering with Minkowski distance functions
Distances in the well known fuzzy c-means algorithm of Bezdek (1973) are measured by the squared Euclidean distance. Other distances have been used as well in fuzzy clustering. For example, Jajuga (1991) proposed to use the L1-distance and Bobrowski and Bezdek (1991) also used the L∞-distance. For the more general case of Minkowski distance and the case of using a root of the squared Minkowski ...
متن کاملEfficient Parallel Algorithms for Euclidean Distance Transform
The Euclidean distance transform (EDT) converts a binary image into one where each pixel has a value equal to its distance to the nearest foreground pixel. Two parallel algorithms for EDT on linear array with reconfigurable pipeline bus system (LARPBS) are presented. For an image with n × n pixels, the first algorithm can complete EDT in O [(log n log log n)/(log log log n)] time using n2 proce...
متن کامل2D Face Recognition Based on PCA & Comparison of Manhattan Distance, Euclidean Distance & Chebychev Distance
This paper is about human face recognition in image files. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. Here, face recognition is done using: (a) Eigen faces and (b) Applying Principal Component Analysis (PCA) on image. The aim is to successfully demonstrate the human face recognition using Principal component...
متن کاملA Micropower Current-Mode Euclidean Distance Calculator for Pattern Recognition
In this paper a new synthesis for circuit design of Euclidean distance calculation is presented. The circuit is implemented based on a simple two-quadrant squarer/divider block. The circuit that employs floating gate MOS (FG-MOS) transistors operating in weak inversion region, features low circuit complexity, low power (<20uW), low supply voltage (0.5V), two quadrant input current, wide dyn...
متن کاملSize-constrained 2-clustering in the plane with Manhattan distance
We present an algorithm for the 2-clustering problem with cluster size constraints in the plane assuming `1-norm, that works in O(n logn) time and O(n) space. Such a procedure also solves a full version of the problem, computing the optimal solutions for all possible constraints on cluster sizes. The algorithm is based on a separation result concerning the clusters of any optimal solution of th...
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: International Journal of Scientific Research in Science, Engineering and Technology
سال: 2020
ISSN: 2394-4099,2395-1990
DOI: 10.32628/ijsrset2073118