K-nearest-neighbor algorithm

The PSORT system [61] predicts the localization of proteins from gram-negative bacteria, gram-positive bacteria, yeasts, animals, and plants. For a query sequence the program calculates the values of feature variables that reflect various characteristics of the sequence (table 10.2). Next, it uses the k-nearest-neighbor algorithm to interpret the set of values obtained and estimates the likelihood of the protein being sorted to each candidate site. Finally, it displays some of the most probable sites. 

When applied to QSAR studies, the activity of molecule u is calculated simply as the average activity of the K nearest neighbors of molecule u. An optimal K value is selected by the optimization through the classification of a test set of samples or by the leave-one-out cross-validation. Many variations of the kNN method have been proposed in the past, and new and fast algorithms have continued to appear in recent years. The automated variable selection kNN QSAR technique optimizes the selection of descriptors to obtain the best models [20]. 

Miller, D. W. (2001) Results of a new classification algorithm combining K nearest neighbors and recursive partitioning../. Chem Inf. Comput. Sci. 41, 168-175. 

Algorithm Combining K Nearest Neighbors and Recursive Partitioning. 

The k-Nearest Neighbors (kNN) algorithm predicts the class of a point in a feature space based on the known attributes of the neighboring k number of points in this space. For instance, say we want to predict tissue status based on a set of independent bioimpedance features, such as the Cole parameters. Using a dataset of measurements with known tissue states, the kNN algorithm will first construct class-labeled vectors in a multidimensional space with one dimension for each feature. Class prediction of a new measurement will then be done based on the majority of class-memberships of the k number of nearest neighbors based on the distance (usually Euclidian) to the new point. 

Find the list L, of K nearest neighbors j of each particle i in / ciust radius and sort out the list in ascending order according to the distance between i and j particles. Thus L,(k) = j and k is the position of the particle j in the list. This procedure can be performed in parallel along with computation of forces in FPM code. To reduce the communication overhead, we use the parallel clustering algorithm off-line after simulation. 

Text-mining methods employ algorithms that rrse similarity-based functions in order to obtain k nearest neighbors for novel query objects [32], Term weighting is performed to measrrre the importance of a term in representing the information contained in the docirment [33], For mining literature, the two most common approaches are ML-based and the rule-based approaches, though in practice a combination of approaches works best [34],... 

Hoffman> B.> Cho> S. J.> Zheng> W.> Wyrick> S.> Nichols> D. E.> Mailman> R. B.> et al. (1999). Quantitative structure-activity relationship modeling of dopamine D(l) antagonists using comparative molecular field analysis> genetic algorithms-partial least-squares, and K nearest neighbor methods. The Journal of Medicinal Chemistry, 42, 3217. 

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