In this paper a hybrid system and a hierarchical neural-net approaches are proposed to solve the automatic labeling problem for unsupervised clustering. The first method consists in the application of nonneural clustering algorithms directly to the output of a neural net; the second one is based on a multilayer organization of neural units. Both methods are a substantial improvement with respec...

Owning to the great growth of e-learning objects, authorities (e.g. ADL and IEEE) have developed some metadata standards to facilitate the keyword search for various e-learning applications. However, too much fields, such as 58 blank fields in IEEE LOM, waiting for authors or annotators to fill up become an endless nightmare. In order to reach our vision of sharing and reusing valuable assets, ...

Clustering is an important technique for understanding of large multi-dimensional datasets. Most of clustering research to date has been focused on developing automatic clustering algorithms and cluster validation methods. The automatic algorithms are known to work well in dealing with clusters of regular shapes, e.g. compact spherical shapes, but may incur higher error rates when dealing with ...

Most of the classical clustering algorithms are strongly dependent on, and sensitive to, parameters such as number of expected clusters and resolution level. To overcome this drawback, in this paper a Genetic Programming framework, capable of performing an automatic data clustering, is presented. Moreover, a novel way of representing clusters which provides intelligible information on patterns ...

INCLUSive allows automatic multistep analysis of microarray data (clustering and motif finding). The clustering algorithm (adaptive quality-based clustering) groups together genes with highly similar expression profiles. The upstream sequences of the genes belonging to a cluster are automatically retrieved from GenBank and can be fed directly into Motif Sampler, a Gibbs sampling algorithm that ...

Web mining data mining for web data is a key factor of web technologies. Especially, web behavior mining has attracted a great deal of attention recently. Behavior mining involves analyzing the behavior of users, finding patterns of user behavior, and predicting their subsequent behaviors or interests. Web behavior mining is used in web advertising systems or content recommendation systems. To ...

Automatic clustering of utterances can be useful for the modeling of dialogue acts for dialogue applications. Previously, the Chinese restaurant process (CRP), a non-parametric Bayesian method, has been introduced and has shown promising results for the clustering of utterances in dialogue. This paper introduces the infinite HMM, which is also a non-parametric Bayesian method, and verifies its ...

Clustering is an important technique for understanding of large multi-dimensional datasets. Most of clustering research to date has been focused on developing automatic clustering algorithms and cluster validation methods. The automatic algorithms are known to work well in dealing with clusters of regular shapes, e.g. compact spherical shapes, but may incur higher error rates when dealing with ...

This paper addresses the influence of audio segmentation and segment clustering on automatic transcription accuracy for large spoken archives. The work forms part of the ongoing MALACH project, which is developing advanced techniques for supporting access to the world’s largest digital archive of video oral histories collected in many languages from over 52000 survivors and witnesses of the Hol...