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Application of kNearest Neighbor on Feature
 Proceedings of ISCIS98, 13th International Symposium on Computer and Information Sciences
, 1998
"... This paper presents the results of the application of an instancebased learning algorithm kNearest Neighbor Method on Feature Projections (kNNFP) to text categorization and compares it with kNearest Neighbor Classifier (kNN). kNNFP is similar to kNN except it finds the nearest neighbors a ..."
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This paper presents the results of the application of an instancebased learning algorithm kNearest Neighbor Method on Feature Projections (kNNFP) to text categorization and compares it with kNearest Neighbor Classifier (kNN). kNNFP is similar to kNN except it finds the nearest neighbors
KNearest Neighbor for Uncertain Data
"... The classifications of uncertain data become one of the tedious processes in the datamining domain. The uncertain data are contains tuples with different data and thus to find similar class of tuples is a complex process. The attributes which have a higher level of uncertainty needs to be treated d ..."
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neighbor approach. The literature shows that much work has been done in this area but still there are certain performance issues in the k nearest neighbor classifier. K nearest neighbor is one of the important algorithms in top 10 data mining algorithms.
MKNN: Modified KNearest Neighbor
"... Abstract — In this paper, a new classification method for enhancing the performance of KNearest Neighbor is proposed which uses robust neighbors in training data. This new classification method is called Modified KNearest Neighbor, MKNN. Inspired the traditional KNN algorithm, the main idea is cla ..."
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Cited by 4 (1 self)
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Abstract — In this paper, a new classification method for enhancing the performance of KNearest Neighbor is proposed which uses robust neighbors in training data. This new classification method is called Modified KNearest Neighbor, MKNN. Inspired the traditional KNN algorithm, the main idea
Classification with Learning kNearest Neighbors
, 1996
"... The nearest neighbor (NN) classifiers, especially the kNN algorithm, are among the simplest and yet most efficient classification rules and are widely used in practice. We introduce three adaptation rules that can be used in iterative training of a kNN classifier. This is a novel approach both fro ..."
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Cited by 20 (3 self)
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that the classification is based on leads to improved classification accuracy. The performances of the suggested learning rules are compared with the usual kNN rules and the LVQ1 algorithm. 1. Introduction The knearest neighbor decision rule (kNN) [1, pages 69127] is a commonly used classification algorithm
kNearest Neighbors in Uncertain Graphs
"... Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to efficiently answer knearest neig ..."
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Cited by 33 (4 self)
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Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to efficiently answer knearest
Hausdorff Distance with kNearest Neighbors
"... Abstract. Hausdorff distance (HD) is an useful measurement to determine the extent to which one shape is similar to another, which is one of the most important problems in pattern recognition, computer vision and image analysis. Howeverm, HD is sensitive to outliers. Many researchers proposed modifi ..."
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by using knearest neighbors (kNN). We use the average distance from each point in the model image to its kNN in the test image to replace the NN procedures of NNHDs and obtain the Hausdorff distance based on kNN, named kNNHDs. When k = 1, kNNHDs are equal to NNHDs. kNNHDs inherit the properties
Providing Diversity in KNearest Neighbor Query
, 2003
"... Given a point query Q in multidimensional space, KNearest Neighbor (KNN) queries return the K closest answers in the database with respect to Q. ..."
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Cited by 17 (1 self)
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Given a point query Q in multidimensional space, KNearest Neighbor (KNN) queries return the K closest answers in the database with respect to Q.
k Nearest Neighbor Classification across
 In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM
, 2006
"... Distributed privacy preserving data mining tools are critical for mining multiple databases with a minimum information disclosure. We present a framework including a general model as well as multiround algorithms for mining horizontally partitioned databases using a privacy preserving k Nearest Nei ..."
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Distributed privacy preserving data mining tools are critical for mining multiple databases with a minimum information disclosure. We present a framework including a general model as well as multiround algorithms for mining horizontally partitioned databases using a privacy preserving k Nearest
KNearest Neighbor Search for Moving Query Point
 In SSTD
, 2001
"... Abstract. This paper addresses the problem of finding k nearest neighbors for moving query point (we call it kNNMP). It is an important issue in both mobile computing research and reallife applications. The problem assumes that the query point is not static, as in knearest neighbor problem, but v ..."
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Cited by 153 (0 self)
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Abstract. This paper addresses the problem of finding k nearest neighbors for moving query point (we call it kNNMP). It is an important issue in both mobile computing research and reallife applications. The problem assumes that the query point is not static, as in knearest neighbor problem
Results 1  10
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