The Fuzzy Hyperline Segment Neural Network (FHLSNN) pattern classifier utilizes fuzzy set as pattern classes in which each fuzzy set is a union of fuzzy set hyperline segments. The Euclidean distance metric is used to compute the distances to decide the degree of membership function. In this paper, the use of other various distance metrics such as Manhattan, Squared Euclidean, Canberra and Cheb...

Data mining is the process of extracting previously unknown and valid information from large databases. Clustering is an important data analysis and data mining method. It is the unsupervised classification of objects into clusters such that the objects from same cluster are similar and objects from different clusters are dissimilar. Data clustering is a difficult unsupervised learning problem ...

Developing a stable early warning system (EWS) model that is capable to give an accurate prediction is a challenging task. This paper introduces k-nearest neighbour (k-NN) method which never been applied in predicting currency crisis before with the aim of increasing the prediction accuracy. The proposed k-NN performance depends on the choice of a distance that is used where in our analysis; we...

Fuzzy clustering techniques handle the fuzzy relationships among the data points and with the cluster centers (may be termed as cluster fuzziness). On the other hand, distance measures are important to compute the load of such fuzziness. These are the two important parameters governing the quality of the clusters and the run time. Visualization of multidimensional data clusters into lower dimen...

Stroke is a cardiovascular (CVD) disease caused by the failure of brain cells to get oxygen supply pose risk ischemic damage and result in death. This Disease can detect based on similarity symptoms experienced sufferer so that early steps be taking with appropriate counseling treatment. detecting requires machine learning method. In this research, author used one supervised classification meth...

Content based image retrieval (CBIR) from large resources has become a dominant research field and found wide interest nowadays in many applications. In this thesis work, we design and implement a content based image retrieval system that uses color and texture as visual features to describe the content of an image region. We use k-nearest neighbor (knn) and HSV color model to extract feature o...

In this paper we describe Iris recognition using Modified Fuzzy Hypersphere Neural Network (MFHSNN) with its learning algorithm, which is an extension of Fuzzy Hypersphere Neural Network (FHSNN) proposed by Kulkarni et al. We have evaluated performance of MFHSNN classifier using different distance measures. It is observed that Bhattacharyya distance is superior in terms of training and recall t...

TOPSIS is a multicriteria decision making technique based on the minimization of geometric distances that allows the ordering of compared alternatives in accordance with their distances from the ideal and anti-ideal solutions. The technique, that usually measures distances in the Euclidean norm, implicitly supposes that the contemplated attributes are independent. However, as this rarely occurs...

Nearest neighbor is pattern matching method for time series prediction in which most recent values of the time series are compared with previous available values and forecasting is achieved by finding the best match pattern (nearest neighbor). Usually Euclidean distance is used to check the similarity of pattern. In this paper two hybrid criteria of pattern matching are being proposed and evalu...

Clustering web users based on their access patterns is a quite significant task in Web Usage Mining. Further to clustering it is important to evaluate the resulted clusters in order to choose the best clustering for a particular framework. This paper examines the usage of Kullback-Leibler divergence, an information theoretic distance, in conjuction with the k-means clustering algorithm. It comp...