Spherical k-means is a widely used clustering algorithm for sparse and high-dimensional data such as document vectors. While several improvements accelerations have been introduced the original algorithm, not all easily translate to spherical variant: Many acceleration techniques, algorithms of Elkan Hamerly, rely on triangle inequality Euclidean distances. However, uses Cosine similarities ins...

With the acceleration of urbanization, climate problems affecting human health and safe operation cities have intensified, such as heat island effect, haze, acid rain. Using high-resolution remote sensing mapping image data to design scientific efficient algorithms excavate plan urban ventilation corridors improve environment is an effective way solve these problems. In this paper, we use unman...

TAMPERE UNIVERSITY OF TECHNOLOGY Master’s Degree Program in Information Technology Ranganathan, Sindhuja: Improvements to k-means clustering Master’s thesis, 42 November 2013 Major Subject: Software Systems Examiner(s): Professor Tapio Elomaa

The -means algorithm is by far the most widely used method for discovering clusters in data. We show how to accelerate it dramatically, while still always computing exactly the same result as the standard algorithm. The accelerated algorithm avoids unnecessary distance calculations by applying the triangle inequality in two different ways, and by keeping track of lower and upper bounds for dist...

This paper addresses the construction of a short-vector (128D) image representation for large-scale image and particular object retrieval. In particular, the method of joint dimensionality reduction of multiple vocabularies is considered. We study a variety of vocabulary generation techniques: different k-means initializations, different descriptor transformations, different measurement regions...

In a standard cluster analysis, such as k-means, in addition to clusters locations and distances between them, it’s important to know if they are connected or well separated from each other. The main focus of this paper is discovering the relations between the resulting clusters. We propose a new method which is based on pairwise overlapping k-means clustering, that in addition to means of clus...