Recent advances in massively parallel sequencing technology have created new opportunities to probe the hidden world of microbes. Taxonomy-independent clustering of the 16S rRNA gene is usually the first step in analyzing microbial communities. Dozens of algorithms have been developed in the last decade, but a comprehensive benchmark study is lacking. Here, we survey algorithms currently used b...

In this paper we propose a framework for automatic detection, tracking and counting of pedestrians in video sequences. The detection and tracking parts are based on an integrated behavioral model for pedestrian dynamics with standard image processing techniques. The target’s counting method is based on a hierarchical clustering of pedestrian trajectories where the data representation is based o...

Very large enterprises present distinct information management challenges: it is difficult for their Chief Information Officers (CIOs) and information architects to obtain a clear overview of enterprise information assets and it is hard to quickly recognize opportunities for productive data sharing. To support users in these endeavors, we have created Affinity, a tool for visualizing clusters o...

This paper describes a methodology for the application of hierarchical clustering methods to the task of outlier detection. The methodology is tested on the problem of cleaning Official Statistics data. The goal is to detect erroneous foreign trade transactions in data collected by the Portuguese Institute of Statistics (INE). These transactions are a minority, but still they have an important ...

In our study on developing a text mining prototype system, it is needed to group documents according to author’s need. However, Traditional documents clustering are usually considered an unsupervised learning. It cannot effectively group documents under user’s need. To solve this problem, we propose a new documents clustering approach. The main contributions include: (1) Describes user’s need b...

This work is motivated by the following question: given a sample of compositional data trajectories (i.e. sequences of composition measurements along a domain), how can one propose a segmentation procedure leading to homogeneous classes? In other words, our contribution aims at studying statistical methods suited for clustering compositional data, when the observations are constituted by trajec...

We present a new hierarchical clustering criteria which can be applied to data set. This is done after generating an initial partition by using a Topological Self Organizing Map. This criteria contains two terms which take into account two di erent errors simultaneously: the square error of the entire clustering (as the Ward criteria) and the topological structure given by the Self Organizing M...

Traditional multimedia indexing methods are based on the principle of hierarchical clustering of the data space where metric properties are used to build a tree that can then be used to prune branches while processing the queries. However, the performance of these methods will deteriorate rapidly when the dimensionality of the data space is increased. We describe a new hierarchical linear subsp...

In this paper, we propose a novel approach for clustering time series, which combines three well-known aspects: a permutation-based coding of the time series, several distance measurements for discrete distributions and hierarchical clustering using different linkages. The proposed method classifies a set of time series into homogeneous groups, according to the degree of dependency among them. ...

Data sets resulting from physical simulations typically contain a multitude of physical variables. It is, therefore, desirable that visualization methods take into account the entire multi-field volume data rather than concentrating on one variable. We present a visualization approach based on surface extraction from multi-field volume data. The extracted surfaces segment the data with respect ...