One way of representing semantics could be via a high dimensional conceptual space constructed by certain lexical semantic space models. Concepts (words), represented as a vector of other words in the semantic space, can be categorized via clustering techniques into a number of regions reflecting different contexts. The conventional clustering algorithms, e.g., K-means method, however, normally...

One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique are dependent on the initialisation of cluster centres. In this article, two initialisation methods are developed. These methods are particularly suited to problems involving very large data sets. The methods have been applied to di erent data sets and good results are obtained.

Much of the challenge and appeal in remixing music comes from manipulating samples. Typically, identifying distinct samples of a song requires expertise in music production software. Additionally, it is di cult to visualize similarities and di↵erences between all samples of a song simultaneously and use this to select samples. MusicMapper is a web application that allows nonexpert users to find...

This paper proposed improved K-means clustering algorithm based on user tag. It first used social annotation data to expand the vector space model of K-means. Then, it applied the links involved in social tagging network to enhance the clustering performance. Experimental result shows that the proposed improved K-means clustering algorithm based on user tag is effective.

Human action recognition is an important yet challenging task. In this paper, a simple and efficient method based on random forests is proposed for human action recognition. First, we extract the 3D skeletal joint locations from depth images. The APJ3D computed from the action depth image sequences by employing the 3D joint position features and the 3D joint angle features, and then clustered i...

A new algorithm is proposed which accelerates the mini-batch k-means algorithm of Sculley (2010) by using the distance bounding approach of Elkan (2003). We argue that, when incorporating distance bounds into a mini-batch algorithm, already used data should preferentially be reused. To this end we propose using nested mini-batches, whereby data in a mini-batch at iteration t is automatically re...

There is often a large disparity between the size of a game we wish to solve and the size of the largest instances solvable by the best algorithms; for example, a popular variant of poker has about 10 nodes in its game tree, while the currently best approximate equilibrium-finding algorithms scale to games with around 10 nodes. In order to approximate equilibrium strategies in these games, the ...

In this paper we present a new clustering method based on k-means that has been implemented on a video surveillance system. Rekmeans does not require to specify in advance the number of clusters to search for and is more precise than k-means in clustering data coming from multiple Gaussian distributions with different co-variances, while maintaining real-time performance. Experiments on real an...

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...

Learning the number of clusters is a key problem in data clustering. We present dip-means, a novel robust incremental method to learn the number of data clusters that can be used as a wrapper around any iterative clustering algorithm of k-means family. In contrast to many popular methods which make assumptions about the underlying cluster distributions, dip-means only assumes a fundamental clus...