This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (LM). We present an online clustering algorithm for LM based on a feature-based item similarity function. We provide a learning framework for estimating the similarity function and present a fast stochastic gradient-based learning technique. In...

Clustering and outlier detection are important data mining areas. Online clustering and outlier detection generally work with continuous data streams generated at a rapid rate and have many practical applications, such as network instruction detection and online fraud detection. This chapter first reviews related background of online clustering and outlier detection. Then, an incremental cluste...

A streaming data clustering algorithm is presented building upon the density-based self-organizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clus...

For many applications, it is necessary to produce speech transcriptions in a causal fashion. To produce high quality transcripts, speaker adaptation is often used. This requires online speaker clustering and incremental adaptation techniques to be developed. This paper presents an integrated approach to online speaker clustering and adaptation which allows efficient clustering of speakers using...

In today’s applications, massive, evolving data streams are ubiquitous. To gain useful information from this data, real time clustering analysis for streams is needed. A multitude of stream clustering algorithms were introduced. However, assessing the effectiveness of such an algorithm is challenging, because up to now there is no tool that allows a direct comparison of these algorithms. We pre...

For a few years, on-line analysis processing (OLAP) and data mining have known parallel and independent evolutions. Some recent studies have shown the interest of the association of these two fields. Currently, we attend the increase of a more elaborated analysis's need. We think that the idea of coupling OLAP and data mining will be able to fulfill this need. We propose to adopt this coupling ...

An algorithm for performing online clustering on the GPU is proposed which makes heavy use of the atomic operations available on the GPU. The algorithm can cluster multiple documents in parallel in way that can saturate all the parallel threads on the GPU. The algorithm takes advantage of atomic operations available on the GPU in order to cluster multiple documents at the same time. The algorit...

We study two online clustering methods for collaborative filtering. In the first method, we assume that each user is equally likely to belong to one of m clusters of users and that the user’s rating for each item is generated randomly according to a distribution that depends on the item and the cluster that the user belongs to. In the second method, we assume that each user is equally likely to...