A central problem in analysis of gene expression data is clustering of genes with similar expression profiles. In this paper, I describe an hierarchical clustering procedure that is based on simple probabilistic model. This procedure clusters genes with respect to a target classification of conditions, so that genes that are expressed similarly in each group of conditions are clustered together.

A problem with the modelling of clusters as d dimensional centroids is that centroids cannot relay much information about cluster shape i.e. elongated, circular, irregular etc... The Agglomerative-Partitional Clustering (APC) methodology introduced here attempts to remedy this situation by joining together centroids coexisting within regions of relatively high density with line segments. Interc...

We show that for any data set in any metric space, it is possible to construct a hierarchical clustering with the guarantee that for every k, the induced k-clustering has cost at most eight times that of the optimal k-clustering. Here the cost of a clustering is taken to be the maximum radius of its clusters. Our algorithm is similar in simplicity and efficiency to common heuristics for hierarc...

Research on the problem of multicriteria territory partitioning is at its begin. This is mainly due to the fact that it involves tools from fields that are to this day still young. To answer this shortage, we propose an adaptation of a multicriteria clustering method that takes spatial constraints into account. Two variants are described and tested on an illustrative case. This example deals wi...

This study examined Chinese shame concepts. By asking native Chinese to identify terms for shame, we collected 113 shame terms. Hierarchical cluster analysis of sorted terms yielded a comprehensive map of the concept. We found, at the highest abstract level, two large distinctions of " shame state, self-focus " and " reactions to shame, other-focus. " While the former describes various aspects ...

RÉSUMÉ. L’hypothèse de cluster est l’hypothèse fondamentale de l’utilisation du clustering dans la recherche d’information. Elle indique que les documents semblables ont tendance à être pertinents pour la même requête. Des travaux passés testent intensivement cette hypothèse avec les méthodes de la classification ascendante hiérarchique (CAH). Mais leurs conclusions ne sont pas cohérentes en te...

In a hierarchical architecture, higher energy nodes can be used to process and send the information while low energy nodes can be used to perform the sensing in the proximity of the target. This means that creation of clusters and assigning special tasks to cluster heads can greatly contribute to overall system scalability, lifetime, and energy efficiency. Hierarchical routing is an efficient w...

K-Means is an clustering algorithm that is most essential functional to distinctive applications together with color clustering and image segmentation. The dimension of cluster numbers in embedded systems, hardware architecture of hierarchical KMeans (HK-Means) is planned to maintain a maximum cluster number of 1024. A hierarchical memory structure is incorporated to offer a highest bandwidth o...

Data routing in wireless sensor networks must be energy-efficient because tiny sensor nodes have limited power. A cluster-based hierarchical routing is known to be more efficient than a flat routing because only cluster-heads communicate with a sink node. Existing hierarchical routings, however, assume unrealistically large radio transmission ranges for sensor nodes so they cannot be employed i...

Hierarchical clustering represents a family of widely used clustering approaches that can organize objects into a hierarchy based on the similarity in objects’ feature values. One significant obstacle facing hierarchical clustering research today is the lack of general and robust evaluation methods. Existing works rely on a range of evaluation techniques including both internal (no ground-truth...