Results 171 to 180 of about 2,173 (213)
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International Journal of Fuzzy System Applications, 2013
Cluster analysis is a branch of data mining, which plays a vital role in bringing out hidden information in databases. Clustering algorithms help medical researchers in identifying the presence of natural subgroups in a data set. Different types of clustering algorithms are available in the literature. The most popular among them is k-means clustering.
Sampath Sundaram, B. Ramya
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Cluster analysis is a branch of data mining, which plays a vital role in bringing out hidden information in databases. Clustering algorithms help medical researchers in identifying the presence of natural subgroups in a data set. Different types of clustering algorithms are available in the literature. The most popular among them is k-means clustering.
Sampath Sundaram, B. Ramya
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Fast isodata clustering algorithms
Pattern Recognition, 1992Abstract The computational requirements of any clustering method are identified as the major bottleneck in the effective exploratory data analysis task. Partial sum and nearest neighbouring distance methods are proposed to speed up the K-MEANS clustering algorithm with Euclidean distance norm.
N. B. Venkateswarlu, P. S. V. S. K. Raju
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A FAST IMPLEMENTATION OF THE ISODATA CLUSTERING ALGORITHM
International Journal of Computational Geometry & Applications, 2007Clustering is central to many image processing and remote sensing applications. ISODATA is one of the most popular and widely used clustering methods in geoscience applications, but it can run slowly, particularly with large data sets. We present a more efficient approach to ISODATA clustering, which achieves better running times by storing the points
Nargess Memarsadeghi +3 more
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An Isodata algorithm for straight line fitting
Pattern Recognition Letters, 1988Abstract This note describes a method of fitting κ straight lines to a set of data points using an algorithm analogous to the Isodata , or κ-means, clustering technique for partitioning a set of data points into κ compact clusters.
Tsai-Yun Phillips, Azriel Rosenfeld
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A MapReduce based ISODATA algorithm
2012 Third International Conference on Intelligent Control and Information Processing, 2012Cluster analysis is a mathematical method that applied in various fields, such as biology, medicine, business and marketing. ISODATA algorithm is a Clustering algorithm that has been widely used. With the development of information technology, data set expanded dramatically which becomes a great challenge to the traditional algorithm.
Cong Wan, Cuirong Wang, Xin Song
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LoRa Network Planning Based on Improved ISODATA Algorithm
2020 International Conference on Wireless Communications and Signal Processing (WCSP), 2020LoRa is a well-known Low-Power Wide-Area Network (LPWAN) specification that often uses a star topology to directly connect IoT devices to the gateway, which sends data to the network server. In practical deployment, determination of the number of gateways and their locations is the key problem in LoRa network planning.
Yiqing Jin +4 more
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An SR-ISODATA algorithm for IDS alerts aggregation
2014 IEEE International Conference on Information and Automation (ICIA), 2014Intrusion detection Systems(IDS) can produce large amount of alert data which usually possesses the characteristics of high redundancy and high repetition. Such kind of data makes the event processing for network security significantly difficult.
Chun Long +3 more
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Fuzzy and ISODATA classification of face contours
Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), 2005A method derived from fuzzy and ISODATA clustering algorithm is proposed to classify face contours. An improved ASM method is used to get face contours as one of face shape features. By using the modified ISODATA method based on Hausdorff distance, which is more suitable to classify "shape" features, face contours are clustering into 7 classes.
null Hua Gu +2 more
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On the Convergence of the Fuzzy Clustering Algorithm “Fuzzy ISODATA”
ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 1986AbstractIn der vorliegenden Arbeit wird für den unscharfen Partitionierungsalgorithmus “Fuzzy ISODATA” gezeigt, daß ausgehend von beliebigem Startpunkt jeder Häufungspunkt der generierten Folge ein stationärer Punkt des erweiterten, Quadratischen‐Fehlersummen‐Funktionals ist, welches als Partitionierungskriterium dient.
von Trzebiatowski, G., Bank, B.
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An efficient parallel ISODATA algorithm based on Kepler GPUs
2014 International Joint Conference on Neural Networks (IJCNN), 2014ISODATA is a well-known clustering algorithm used in various areas. It employs a heuristic strategy allowing the clusters based on the nearest neighbor rule to split and merge as appropriate. However, since the volume of the data to be clustered in real world is growing continuously, the efficiency of serial ISODATA has become a serious practical issue.
Shiquan Yang +2 more
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