In this paper, algorithms for automatic albuming of consumer photographs are described. Specifically, two core algorithms namely event clustering and screening of low-quality images, are introduced and their performance is evaluated. Event clustering and image quality screening have many applications including albuming services, image management and organization, and digital photofinishing. The...

Organizations as well as personal users invest a great deal of time in assigning documents they read or write to categories. Automatic document classification that matches user subjective classification is widely used, but much challenging research still remain to be done. The self-organizing map (SOM) is an artificial neural network (ANN) that is mathematically characterized by transforming hi...

ProtoNet is an agglomerative binary clustering procedure that provides a hierarchical tree of over 1,000,000 proteins. In this paper we show that the ProtoNet tree is composed of a large number of biologically valid clusters and that the tree expresses functional information. We confirm that several aspects of the tree highly correspond to various external sources, some of which are manually va...

We built a system for the automatic creation of a text-based topic hierarchy, meant to be used in a geographically defined community. This poses two main problems. First, the appearance of both standard language and a community-related dialect, demanding that dialect words should be as much as possible corrected to standard words, and second, the automatic hierarchic clustering of texts by thei...

This paper addresses an automatic classification of preposition types in German, comparing hard and soft clustering approaches and various windowand syntax-based co-occurrence features. We show that (i) the semantically most salient preposition features (i.e., subcategorised nouns) are the most successful, and that (ii) soft clustering approaches are required for the task but reveal quite diffe...

Genetic algorithms (GA) are randomized search and optimization techniques which have proven to be robust and effective in large scale problems. In this work, we propose a new GA approach for solving the automatic clustering problem, ACGA Automatic Clustering Genetic Algorithm. It is capable of finding the optimal number of clusters in a dataset, and correctly assign each data point to a cluster...

It has been widely observed that different NLP applications require different sense granularities in order to best exploit word sense distinctions, and that for many applications WordNet senses are too fine-grained. In contrast to previously proposed automatic methods for sense clustering, we formulate sense merging as a supervised learning problem, exploiting human-labeled sense clusterings as...

The search for interesting information in a huge data collection is a tough job frustrating the seekers for that information. The automatic text summarization has come to facilitate such searching process. Automatic text summarization is to compress an original document into an abridged version by extracting almost all of the essential concepts with text mining techniques. The selection of dist...

This paper investigates the new problem of automatic sense induction for instance names using automatically extracted attribute sets. Several clustering strategies and data sources are described and evaluated. We also discuss the drawbacks of the evaluation metrics commonly used in similar clustering tasks. The results show improvements in most metrics with respect to the baselines, especially ...

In this article a clustering algorithm, allowing the automatic detection of speakers’ register changes, is presented. Together with automatic detection of pause duration, it has shown to be efficient for the automatic detection and prediction of topic changes. The need to take into account other parameters such as tempo and intensity, in the framework of Linear Discriminant Analysis, is propose...