Minimization of the number of cluster heads in a wireless sensor network is a very important problem to reduce channel contention and to improve the efficiency of the algorithm when executed at the level of cluster-heads. This paper proposes a Self Organizing Sensor (SOS) network based on an intelligent clustering algorithm which does not require many user… (More)
This study deals with information organization for more efficient Internet document search and browsing results. As the appropriate algorithm for this purpose, this study proposes the heuristic algorithm, which functions similarly with the star clustering algorithm but performs a more efficient time complexity of O(kn),(k<<n) instead of O(n²) found in the… (More)
Document clustering or unsupervised document classification has been used to enhance information retrieval. Recently this has become an intense area of research due to its practical importance. Outliers are the elements whose similarity to the centroid of the corresponding category is below some threshold value. In this paper, we show that excluding… (More)
Wireless sensor networks are composed of a huge number of sensor nodes, which have limited resources-energy, memory and computation power. Energies are directly related to the lifetime of sensor network. If sensor nodes can be grouped to clusters, cluster member sensor nodes only need to communicate with cluster center (head) and this leads to energy… (More)
MEDLINE is a very large database of abstracts of research papers in medical domain, maintained by the National Library of Medicine. Documents in MEDLINE are supplied with manually assigned keywords from a controlled vocabulary called MeSH terms, classified for each document into major MeSH terms describing the main topics of the document and minor MeSH… (More)
MEDLINE is a representative collection of medical documents supplied with original full-text natural-language abstracts as well as with representative keywords (called MeSH-terms) manually selected by the expert annotators from a pre-defined ontology and structured according to their relation to the document. We show how the structured manually assigned… (More)
We show that excluding outliers from the training data significantly improves kNN classifier, which in this case performs about 10% better than the best know method—Centroid-based classifier. Outliers are the elements whose similarity to the centroid of the corresponding category is below a threshold.