The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining te...

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A popul...

One of the most known and effective methods in supervised classification is the K-Nearest Neighbors classifier. Several approaches have been proposed to enhance its precision, with the Fuzzy K-Nearest Neighbors (Fuzzy-kNN) classifier being among the most successful ones. However, despite its good behavior, Fuzzy-kNN lacks of a method for properly defining several mechanisms regarding the repres...

This paper presents a new feature selection method by modifying fitness function of genetic algorithm. Our implementation environment is a face recognition system which uses genetic algorithm for feature selection and k-Nearest Neighbor as a classifier together with our proposed Swap Training. In each iteration of genetic algorithm for assessment of one specific chromosome, swaps training switc...

Cross-validation is an established technique for estimating the accuracy of a classifier and is normally performed either using a number of random test/train partitions of the data, or using kfold cross-validation. We present a technique for calculating the complete cross-validation for nearest-neighbor classifiers: i.e., averaging over all desired test/train partitions of data. This technique ...

A reverse k nearest neighbor (RkNN) query retrieves all the data points that have q as one of their k closest points. In recent years, considerable research has been conducted into monitoring reverse k nearest neighbor queries. In this paper, we study the problem of continuous reverse nearest neighbor queries where both the query object q and data objects are moving. Existing state-of-the-art t...

Social media used in communicating that is very popular Indonesia. One of the most Twitter. Twitter a social site where people can share information publicly. This be processed to make sentiment analysis. research attempts create system detect positive or negative sentiments public information. The method for this classification comparison Naive Bayes Classifier and K-Nearest Neighbor using TF-...

In general there are two main approaches for overcoming the highdimensional and small sample size (SSS) problem. One is to apply feature extraction or selection to reduce the dimensionality, and then applying the reduced-dimensionality data set to classifier. The other is to modify the classifier design to be suitable for SSS problem. This study integrates the two approaches into a new K-neares...

This paper studies a new form of nearest neighbor queries in spatial databases, namely, mutual nearest neighbor (MNN) search. Given a set D of objects and a query object q, an MNN query returns from D, the set of objects that are among the k 1 (P1) nearest neighbors (NNs) of q; meanwhile, have q as one of their k 2 (P1) NNs. Although MNN queries are useful in many applications involving decisio...