In the k-nearest neighbor (KNN) classifier, nearest neighbors involve only labeled data. That makes it inappropriate for the data set that includes very few labeled data. In this paper, we aim to solve the classification problem by applying transduction to the KNN algorithm. We consider two groups of nearest neighbors for each data point — one from labeled data, and the other from unlabeled dat...

In this paper we describe a method for hybridizing a genetic algorithm and a k nearest neighbors classification algorithm. We use the genetic algorithm and a training data set to learn real-valued weights associated with individual attributes in the data set. We use the k nearest neighbors algorithm to classify new data records based on their weighted distance from the members of the training s...

Naive Bayes Nearest Neighbor (NBNN) has been proposed as a powerful, learning-free, non-parametric approach for object classification. Its good performance is mainly due to the avoidance of a vector quantization step, and the use of image-to-class comparisons, yielding good generalization. In this paper we study the replacement of the nearest neighbor part with more elaborate and robust (sparse...

In this paper we study random forests through their connection with a new framework of adaptive nearest neighbor methods. We first introduce a concept of potential nearest neighbors (k-PNN’s) and show that random forests can be seen as adaptively weighted k-PNN methods. Various aspects of random forests are then studied from this perspective. We investigate the effect of terminal node sizes and...

The clustering over various granularities for high dimensional data in arbitrary shape is a challenge in data mining. In this paper Nearest Neighbors Absorbed First (NNAF) clustering algorithm is proposed to solve the problem based on the idea that the objects in the same cluster must be near. The main contribution includes:(1) A theorem of searching nearest neighbors (SNN) is proved. Based on ...

2 KNN Algorithm 2 2.

The growing information infrastructure in a variety of disciplines involves an increasing requirement for efficient data mining techniques. Fast dimensionality reduction methods are important for understanding and processing of large data sets of high-dimensional patterns. In this work, unsupervised nearest neighbors (UNN), an efficient iterative method for dimensionality reduction, is presente...

In order to deliver the promise of Moore’s Law to the end user, compilers must make decisions that are intimately tied to a specific target architecture. As engineers add architectural features to increase performance, systems become harder to model, and thus, it becomes harder for a compiler to make effective decisions. Machine-learning techniques may be able to help compiler writers model mod...

The algorithm is discussed in the context of one of the practical applications: aligning DNA reads to a reference genome. An implementation of the algorithm is shown to align about 106 reads per CPU minute and about 108 base-pairs per CPU minute (human DNA reads). This implementation is compared to the popular software packages Bowtie and BWA, and is shown to be over 5−10 times faster in some a...

We introduce a new method for nding several types of optimal k-point sets, minimizing perimeter, diameter, circumradius, and related measures, by testing sets of the O(k) nearest neighbors to each point. We argue that this is better in a number of ways than previous algorithms, which were based on high order Voronoi diagrams. Our technique allows us for the rst time to e ciently maintain minima...