Video decomposition techniques are fundamental tools for allowing effective video browsing and re-using. In this work, we consider the problem of segmenting broadcast videos into coherent scenes, and propose a scene detection algorithm based on hierarchical clustering, along with a very fast state-of-the-art shot segmentation approach. Experiments are performed to demonstrate the effectiveness ...

Identifying bidders with suspicious bidding activities related to possible online auction fraud is a difficult task due to a large number of users participating in online auctions. In order to reduce the number of users to be investigated, we examine observable features of a bidder’s behavior, and utilize a hierarchical clustering technique to divide a collection of bidders into normal and devi...

CFSFDP (clustering by fast search and find of density peaks) is recently developed density-based clustering algorithm. Compared to DBSCAN, it needs less parameters and is computationally cheap for its noniteration. Alex. at al have demonstrated its power by many applications. However, CFSFDP performs not well when there are more than one density peak for one cluster, what we name as "no density...

In this paper we apply various clustering algorithms to the dialect pronunciation data. At the same time we propose several evaluation techniques that should be used in order to deal with the instability of the clustering techniques. The results have shown that three hierarchical clustering algorithms are not suitable for the data we are working with. The rest of the tested algorithms have succ...

We study hierarchical clustering schemes under an axiomatic view. We show that within this framework, one can prove a theorem analogous to one of Kleinberg (2002), in which one obtains an existence and uniqueness theorem instead of a non-existence result. We explore further properties of this unique scheme: stability and convergence are established. We represent dendrograms as ultrametric space...

In this work, we focus on the analysis of process schemas in order to extract common substructures. In particular, we represent processes as graphs, and we apply a graph-based hierarchical clustering technique to group similar sub-processes together at different levels of abstraction. We discuss different representation choices of process schemas that lead to different outcomes.

At the beginning, each point is treated as an individual cluster. In each step, two clusters with the largest similarity are merged into a new one. The backward strategy is applied after the generation of a new cluster. In the example, in steps (b)–(f), no boundary document is found in the generated cluster and reallocation does not occur. In step (g), two documents located in the middle are me...

In the photo retrieval task of ImageCLEF 2008, we examined the influences of image representations and clustering methods for enhancing instance recall when they are used in post-retrieval clustering. Two types of visual concepts and hierarchical and partitioning clustering methods were compared. We used the title fields in the search topics, and either only the title fields or both the title a...

In applications such as video post-production users are confronted with large amounts of redundant unedited raw material, called rushes. Viewing and organizing this material are crucial but time consuming tasks. Typically multiple but slightly different takes of the same scene can be found in the rushes video. We propose a method for detecting and clustering takes of one scene shot from the sam...

This paper provides an innovative segmentation approach stemming from the combination of cluster analyses and fuzzy learning techniques. Our research provides a real case solution in the Spanish energy market to respond to the increasing number of requests from industry managers to be able to interpret ambiguous market information as realistically as possible. The learning stage is based on the...