The phylogenomic classification of protein sequences attempts to categorize a given protein within the evolutionary context of the entire family. It involves mainly four steps: selection of homologous sequences, multiple sequence alignment, phylogenetic tree construction and tree-based classification. This supposes that the tree used as a basis of protein classification is correct. Sequence ali...

A new approach, based on the k-Nearest Neighbor (kNN) classifier, is used to classify program behavior as normal or intrusive. Program behavior, in turn, is represented by frequencies of system calls. Each system call is treated as a word and the collection of system calls over each program execution as a document. These documents are then classified using kNN classifier, a popular method in te...

This paper presents a decision support tool for educational and vocational guidance, based on the supervised classification method k-nearest neighbors (KNN). This method consists in determining, for each new observation to be classified, the list of nearest neighbors of the observations already classified. The use of the KNN method requires choosing a distance and the most classical one is the ...

Software Architecture is important factor for the development of complex and big software system. Software Architecture Decomposition is an important part in software design. Software clustering is used to cluster functions of similar type in one cluster and other are in other cluster. Kmean is the base of the clustering but it has some limitations. Many clustering methods are used for decompos...

Data Mining has great scope in the field of medicine. In this article we introduced one new fuzzy approach for prediction of hepatitis disease. Many researchers have proposed the use of K-nearest neighbor (KNN) for diabetes disease prediction. Some have proposed a different approach by using K-means clustering for reprocessing and then using KNN for classification. In our approach Naive Bayes c...

While k-nearest neighbor queries are becoming increasingly common due to mobile and geospatial applications, orthogonal range queries in high-dimensional data are extremely important in scientific and web-based applications. For efficient querying, data is typically stored in an index optimized for either kNN or range queries. This can be problematic when data is optimized for kNN retrieval and...

Support vector machine (SVM) is one of the most powerful supervised learning algorithms in gene expression analysis. The samples intermixed in another class or in the overlapped boundary region may cause the decision boundary too complex and may be harmful to improve the precise of SVM. In the present paper, hybridized k-nearest neighbor (KNN) classifiers and SVM (HKNNSVM) is proposed to deal w...

k-Nearest Neighbor (KNN) is one of the most popular algorithms for pattern recognition. Many researchers have found that the KNN algorithm accomplishes very good performance in their experiments on different data sets. The traditional KNN text classification algorithm has three limitations: (i) calculation complexity due to the usage of all the training samples for classification, (ii) the perf...