Results 161 to 170 of about 7,930 (201)
Assessment of forest fragmentation in the REDD+ priority zones using two land use/land cover (LULC) sources in the tropical Andean landscape of Ecuador. [PDF]
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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.
S. Sampath, 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.
S. Sampath, 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 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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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
Memarsadeghi, Nargess +3 more
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Binary tree design using fuzzy isodata
Pattern Recognition Letters, 1986A procedure for designing a fuzzy binary decision tree using unlabeled samples is developed. At each node, the data is split into two dissimilar groups using the fuzzy Isodata algorithm. All the available features are used while clustering. Then, the best feature among these available features is selected based on some separation index.
B Bharathi Devi, V.V.S Sarma
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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 Local Optimality of the Fuzzy Isodata Clustering Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986The convergence of the fuzzy ISODATA clustering algorithm was proved by Bezdek [3]. Two sets of conditions were derived and it was conjectured that they are necessary and sufficient for a local minimum point. In this paper, we address this conjecture and explore the properties of the underlying optimization problem.
Selim, Shokri Z., Ismail, M. A.
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Supervising ISODATA with an information theoretic stopping rule
Pattern Recognition, 1990Abstract New biomedical imaging modalities, such as Magnetic Resonance Imaging (MRI), provide fertile multidimensional environments for the automatic identification of biological soft tissues, but lack the a priori information required to appropriately train supervised classifiers.
Charles S. Carman, Michael B. Merickel
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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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