Users prefer to navigate subjects from organized topics in an abundance resources than to list pages retrieved from search engines. We propose a framework to cluster frequent itemsets (sets of common words) into topics, produce a hierarchical list, and then generate topics sequence from a collection of documents. The framework will regenerate a next sequence when users click a topic. Consider b...

In the hierarchical wireless sensor network (WSN), cluster-based network architecture can enhance network self-control capability and resource efficiency, and prolong the whole network lifetime. Thus, clustering has also been a topic of interest in many different disciplines. Finding an energy-effective and efficient way to generate cluster is very important in WSN. We propose a distributed, en...

This paper addresses the problem of cluster defining criteria by proposing a model-based characterization of interpattern relationships. Taking a dissimilarity matrix between patterns as the basic measure for extracting group structure, dissimilarity increments between neighboring patterns within a cluster are analyzed. Empirical evidence suggests modeling the statistical distribution of these ...

Agglomerative hierarchical cluster analysis was used to group similar spectra from a large database of samples. Based on angles between reflectance vectors of members of a cluster, a reflectance vector was selected as representative of that cluster. Representative samples were grouped together and stored as new calibration targets. Simulated wide-band imaging with glass filters was performed us...

Disease mapping aims to estimate the spatial pattern in disease risk across an area, identifying units which have elevated disease risk. Existing methods use Bayesian hierarchical models with spatially smooth conditional autoregressive priors to estimate risk, but these methods are unable to identify the geographical extent of spatially contiguous high-risk clusters of areal units. Our proposed...

q-mode hierarchical cluster (hca) and principal component analysis (pca) were simultaneously applied to groundwater hydrochemical data from the three times in 2004: june, september, and december, along the ain azel aquifer, algeria, to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the h...

Bioinformatics emerged as a challenging new area of research and brought forth numerous computational problems. Here computers are used to gather, store, analyze and merge biological data. In this paper, the problem of clustering interval-scaled data and sequence data is analyzed in a new approach using Hierarchical Sequence Clustering. In Sequence clustering, it is necessary to find the simila...

Mood profiling has been a popular assessment strategy since the 1970s, although little evidence exists of distinct mood profiles beyond the realm of sport and exercise. In the present study, we investigated clusters of mood profiles derived from the six subscales of the Brunel Mood Scale using the In The Mood website. Mood responses in three samples (n = 2,364, n = 2,303, n = 1,865) were analyz...

In the hierarchical wireless sensor network (WSN), selecting cluster head (CH) is important issue to increase the network energy efficiency, scalability and lifetime. For the sake of balancing energy expenditure of sensor nodes and improving the performance of routing, We propose a distributed and self-adaptive cluster-head selection algorithm. Based on the hierarchical agglomerative clustering...

This paper introduces a variant of agglomerative hierarchical clustering techniques. The new technique is used for categorizing character shapes (allographs) in large data sets of handwriting into a hierarchical structure. Such a technique may be used as the basis for a systematic naming scheme of character shapes. Problems with existing methods are described and the proposed method is explaine...