k-nearest neighbor clustering
Encodings and models for antimicrobial peptide classification for multi-resistant pathogens
... the k-nearest neighbor clustering to ag- gregate amino acids into five classes using their amino acid index, ...tioned clustering by aggregating pairs of amino acids, such as TT, TH, ...
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Title: AN ADVANCE APPROACH IN CLUSTERING HIGH DIMENSIONAL DATA
... data clustering, hubness information is drawn from k nearest- neighbor lists, which have been used in the past to perform clustering in various ...the k-nearest neighbors. ...
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Species Identification Using Part of DNA Sequence: Evidence from Machine Learning Algorithms
... Cluster-K-Nearest Neighbor (C-K-NN) [13], [14] is a classifier that combines two algorithms; the K-means modified algorithm [15] and the K-Nearest ...using K-means. ...
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Nearest Neighbor Search with Anytime Clustering Method
... during k- NN ...the nearest neighbors of a given data point is needed and mainly it depends upon clustering ...function, k-means, ONE (Online Nearest-neighbor Estimation), ...
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A Deterministic K means Algorithm based on Nearest Neighbor Search
... used clustering methods is k-means clustering algorithm ...by K-means. Centroids of all sub-samples are then clustered together by K-means using the K centroids of each sub- ...
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K-NEAREST NEIGHBOR FOR BEST CLUSTERING STATION IN SELECTION OF ACCESS POINTS ON THE G MULTI RATE WIRELESS NETWORK
... Value range of SNR between 30 until 80, range of transmission rate are in value 6, 9, 12, 18, 24, 36, 48 and 54. By determining K = n-1 for each AP, 1 Sta_best is chosen as the cluster center, determined based on ...
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Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation
... commonly found in the Pattern Recognition field. This decision has been mo- tivated by the fact that with the threshold p < 0.05 all cluster-augmentation cases (ckNN+) significantly improved over the initial ...
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USING AN ANT COLONY OPTIMIZATION ALGORITHM FOR IMAGE EDGE DETECTION AS A THRESHOLD SEGMENTATION FOR OCR SYSTEM
... [4]. Clustering is the process of making a group so that all members of each partition has a similarity based on certain matrix and a number of k in the data ...resemblance. K-means algorithm ...
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Optimal Clustering with Nearest Neighbor Relationships
... in k-nearest neighbor lists of other points than the rest of the points from the set, can in fact be used for ...reverse nearest neighbor topology of protein interaction ...in ...
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DBSCAN-BRNNDE: A Density-based Clustering Algorithm using Bichromatic Reverse nearest Neighbor Density Estimates
... mining, clustering algorithms is one of mostly used area for many ...Several clustering algorithms have been introduced, such as the evolutionary methods and spectral clustering, however none of the ...
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Clustering with shared nearest neighbor unscented transform based estimation
... K-means type algorithm which is linear with respect to the number of the data points in [4] is also useful in describing the cluster formation in terms of attributes contribution to dissimilar clusters. These ...
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Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions
... generic clustering algorithm we studied in this article is nearest neighbor clustering, which produces clusterings that are constant on small local ...popular clustering objective ...
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Evaluating the Performance of Dual Weighted K Nearest Neighbor Classifier
... When k=1, the simplest form of KNN is called as the nearest neighbor rule [6] ...that k=1 is the optimal value for some distribution. If „k' is very small the classification results are ...
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Exact fuzzy k Nearest neighbor classification for big datasets
... However, the kNN algorithm gives the same importance to every neighbor, assuming that the boundaries between classes are perfectly defined, which is not always true. There is an effective improvement of kNN that ...
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Spatio-Temporal Query Processing on Weighted Timestamp Data Environment
... Lee et al. [4] were the first to study the consistent top-k query, which is the special case of DTop-k with the durability threshold r fixed to 100 percent. In the example of consistent top-2 query with ...
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k-Expected Nearest Neighbor Search over Gaussian Objects
... Based on the above analyses, we conclude that PLUB achieves the best performance in efficiency because it uses both lower bound and upper bound for filtering. AVG is a better choice than PLB when k is larger than ...
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Pruned fuzzy K nearest neighbor classifier for beat classification
... Simple K-Nearest Neighbor (SKNN) classifier used in our previous work offers many advantages over other classifiers including simplicity and ease of parallel implementation, adaptability and online ...
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Exact fuzzy k-Nearest neighbor classification for big datasets
... the most effective methods in supervised learning problems. It classifies unseen cases comparing their similarity with the training data. Nevertheless, it gives to each labeled sample the same importance to classify. ...
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Public Bicycle Site Area Division Based On Improved K - Means Algorithm
... traditional K means clustering algorithm as the foundation, this paper proposes a new optimization based on clustering center, improve scheduling demand for K - means ...number K is ...
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Application of KNN-Genetic Algorithm for Analysing Student Learning in Educational Data Mining Paradigm
... K-Nearest neighbor works on the concept of classification of the instances which are closely situated to the specified class are likely belong to the same class rather than the instance which are ...
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