In response to the rapid growth of many sorts information, highway data has continued evolve in direction big terms scale, type, and structure, exhibiting characteristics multi-source heterogeneous data. The k-nearest neighbor (KNN) join received a lot interest recent years due its wide range applications. Processing KNN joins is time-consuming inefficient quadratic structure method . As number...

K-nearest neighbor classification algorithm is one of the most basic algorithms in machine learning, which determines sample's category by similarity between samples. In this paper, we propose a quantum with Hamming distance. algorithm, computation firstly utilized to obtain distance parallel. Then, core sub-algorithm for searching minimum unordered integer sequence presented find out Based on ...

The k-nearest neighbor rule is one of the simplest and most attractive pattern classification algorithms. It can be interpreted as an empirical Bayes classifier based on the estimated a posteriori probabilities from the k nearest neighbors. The performance of the k-nearest neighbor rule relies on the locally constant a posteriori probability assumption. This assumption, however, becomes problem...

This paper describes a comparison of approaches for time series classification. Our comparisons included two different outlier removal methods (discords and reverse nearest neighbor), two different distance measures (Euclidean distance and dynamic time warping), and two different classification algorithms (k nearest neighbor and support vector machines). An algorithm for semi-supervised learnin...

Usually, objects to be classified are represented by features. In this paper, we discuss an alternative object representation based on dissimilarity values. If such distances separate the classes well, the nearest neighbor method offers a good solution. However, dissimilarities used in practice are usually far from ideal and the performance of the nearest neighbor rule suffers from its sensitiv...

The recognition of handwritten numeral is an important area of research for its applications in post office, banks and other organizations. This paper presents automatic recognition of handwritten Kannada numerals based on structural features. Five different types of features, namely, profile based 10-segment string, water reservoir; vertical and horizontal strokes, end points and average bound...

We present new nearest neighbor methods for text classification and an evaluation of these methods against the existing nearest neighbor methods as well as other well-known text classification algorithms. Inspired by the language modeling approach to information retrieval, we show improvements in k-nearest neighbor (kNN) classification by replacing the classical cosine similarity with a KL dive...

Nearest neighbor classification methods are a useful and a relatively straightforward to implement classification technique. However, despite such appeal, they still suffer from the curse of dimensionality. Additionally, the nature of the data sets may not be wholly applicable to the model assumed in the nearest neighbor methods. As such there have been many proposed optimizations. Two such opt...

Nearest neighbor classification assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose a locally adaptive nearest neighbor classification method to try to minimize bias. We use a Chi-sq...