Clustering of relational data has so far received a lot less attention than classification of such data. In this paper we investigate a simple approach based on randomized propositionalization, which allows for applying standard clustering algorithms like KMeans to multi-relational data. We describe how random rules are generated and then turned into boolean-valued features. Clustering generall...

The traditional clustering algorithm, K-means, is famous for its simplicity and low time complexity. However, the usability of K-means is limited by its shortcoming that the clustering result is heavily dependent on the user-defined variants, i.e., the selection of the initial centroid seeds and the number of clusters (k). A new clustering algorithm, called K-means+, is proposed to extend K-mea...

Time series have become an important class of temporal data objects in our daily life while clustering analysis is an effective tool in the fields of data mining. However, the validity of clustering time series subsequences has been thrown into doubts recently by Keogh et al. In this work, we review this problem and propose the phase shift weighted spherical k-means algorithm (PS-WSKM in abbrev...

One day, you will discover a new adventure and knowledge by spending more money. But when? Do you think that you need to obtain those all requirements when having much money? Why don't you try to get something simple at first? That's something that will lead you to know more about the world, adventure, some places, history, entertainment, and more? It is your own time to continue reading habit....

In this paper we introduce a new single pass clustering algorithm called GenIc designed with the objective of having low overall cost. We examine some of the properties of GenIc and compare it to windowed k-means. We also study its performance using experimental data sets obtained from network monitoring.

The goal of this paper is to evaluate the contribution of speaker change detection (SCD) to the performance of a speaker diarization system in the telephone domain. We compare the overall performance of an i-vector based system using both SCD-based segmentation and a naive constant length segmentation with overlapping segments. The diarization system performs K-means clustering of i-vectors whi...

The k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and are inefficient for solving clustering problems in large datasets. Recently, incremental approaches have been developed to resolve difficulties with the choice of starting points. The global k-means and the modified global k-means algorithms are b...

ATMs enable the public to perform €nancial transactions. Banks try to strategically position their ATMs in order to maximize transactions and revenue. In this paper, we introduce a model which provides a score to an ATM location, which serves as an indicator of its relative likelihood of transactions. In order to eciently capture the spatially dynamic features, we utilize two concurrent predic...

Documents in HTML format have many features to analyze, from the terms in special sections to the phrases that appear in the whole document. However, it is important to decide which feature contributes the most to separate documents according to classes. Given this information, it is possible not to include certain feature in the representation for the document, given that it is expensive to co...

1. Texture segmentation can lead to multiscale outputs in which the partitions at successive scales are nested. 2. We can incorporate hierarchical segmentation into a K-Means clustering technique by steadily relaxing inter-cluster distances. 3. Thus, it is possible to hierarchically segment images based solely on texture measurements. 4. This hierarchical, multiscale segmentation is useful in i...