This work focuses on the active selection of pairwise constraints for spectral clustering. We develop and analyze a technique for Active Constrained Clustering by Examining Spectral eigenvectorS (ACCESS) derived from a similarity matrix. The ACCESS method uses an analysis based on the theoretical properties of spectral decomposition to identify data items that are likely to be located on the bo...

Information about the state and planning of the speaker is obscured in traditional classifications of disfluencies which are generally at the word level. This study delves into the acoustic and prosodic information of repetitions, one of the most common disfluencies. A hierarchical clustering of prosodic features reveals three subsets of repetitions, each reflecting different problems in planning.

In this study we apply hierarchical spectral partitioning of bipartite graphs to a Dutch dialect dataset to cluster dialect varieties and determine the concomitant sound correspondences. An important advantage of this clustering method over other dialectometric methods is that the linguistic basis is simultaneously determined, bridging the gap between traditional and quantitative dialectology. ...

This paper presents the first participation of the University of Ottawa group in the Photo Retrieval task at Image CLEF 2008. Our system uses the following components: Lucene for text indexing and LIRE for image indexing. We experiment with several clustering methods in order to retrieve images from diverse clusters. The clustering methods are: k-means clustering, hierarchical clustering, and o...

We create a weighted lexical network derived from the cosine similarities of financial news feeds to compare two clustering methods, Newman's Modularity method and hierarchical clustering. We find that hierarchical clustering, clustering documents according to shared unique terms, shows results that are closer to expectation.

clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. the aim of clustering is to collect data points. in this paper, a cultural algorithm (ca) is presented to optimize partition with n objects into k clusters. the ca is one of the effective methods for searching into the problem space in order to find a n...

Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a small subset of pairwise similarities, significantly less than the complete set of N(N...

In the MediaEval 2016 Retrieving Diverse Social Images Task, we proposed a general framework based on agglomerative hierarchical clustering (AHC). We tested the provided credibility descriptors as a vector input for our AHC. The results on devset showed that this vector based on the credibility descriptors is the best feature, but unfortunately that is not confirmed on testset. To merge several...

Current state-of-the-art fish monitoring systems are lack of intelligent in interpreting fishes behaviors automatically. To tackle these problems, we propose a vision-based method that automatically analyze behaviors of a group of fishes in an aquarium and detect abnormality precisely. Here we consider the problem in two steps. First, we propose a new incremental spectral clustering method to e...

The paper considers techniques for grouping objects that are described with many quantitative and qualitative attributes and may exist in several copies. Such multi-attribute objects may be represented as multisets or sets with repeating elements. Multiset characteristics and operations under an arbitrary number of multisets are determined. The various options for the objects’ aggregation (addi...