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Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 599 (51 self)
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, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted
Finding Intensional Knowledge of DistanceBased Outliers
 In VLDB
, 1999
"... Existing studies on outliers focus only on the identification aspect; none provides any intensional knowledge of the outliersby which we mean a description or an explanation of why an identified outlier is exceptional. For many applications, a description or explanation is at least as vital to t ..."
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Cited by 80 (1 self)
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to the user as the identification aspect. Specifically, intensional knowledge helps the user to: (i) evaluate the validity of the identified outliers, and (ii) improve one's understanding of the data. The two main issues addressed in this paper are: what kinds of intensional knowledge to provide, and how
Algorithms for Speeding up Distancebased Outlier Detection
 ACM KDD Conference
, 2011
"... The problem of distancebased outlier detection is difficult to solve efficiently in very large datasets because of potential quadratic time complexity. We address this problem and develop sequential and distributed algorithms that are significantly more efficient than stateoftheart methods while ..."
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Cited by 7 (2 self)
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The problem of distancebased outlier detection is difficult to solve efficiently in very large datasets because of potential quadratic time complexity. We address this problem and develop sequential and distributed algorithms that are significantly more efficient than stateoftheart methods
Relational DistanceBased Clustering
, 1998
"... Work on firstorder clustering has primarily been focused on the task of conceptual clustering, i.e., forming clusters with symbolic generalizations in the given representation language. By contrast, for propositional representations, experience has shown that simple algorithms based exclusively on ..."
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Cited by 35 (0 self)
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on distance measures can often outperform their conceptbased counterparts. In this paper, we therefore build on recent advances in the area of firstorder distance metrics and present RDBC, a bottomup agglomerative clustering algorithm for firstorder representations that relies on distance information only
Distancebased adaptive kneighborhood selection
, 2013
"... The knearest neighbor classifier follows a simple, yet powerful algorithm: collect the k data points closest to an unlabeled instance, according to a given distance measure, and use them to predict that in stance’s label. The two components, the parameter k governing the size of used neighborhood ..."
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with existing work and especially that the recent techniques do not constitute an improvement. CR Subject Classification: I.2, H.2.8 Distancebased adaptive kneighborhood selection
Algorithms for Detecting DistanceBased Outliers in High Dimensions
"... In this paper, we study efficient algorithms for detecting distancebased outliers from a set of n ddimensional points with respect to two threshold integer parameters k and M, and a radius parameter r. First, we design an algorithm utilizing ddimensional balls to facilitate fast local search, ach ..."
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In this paper, we study efficient algorithms for detecting distancebased outliers from a set of n ddimensional points with respect to two threshold integer parameters k and M, and a radius parameter r. First, we design an algorithm utilizing ddimensional balls to facilitate fast local search
DistanceBased Locality Analysis and Prediction
"... Profiling can accurately analyze program behavior for select data inputs. This article shows that profiling can also predict program locality for inputs other than profiled ones. Here locality is defined by the distance of data reuse. The article describes three distancebased techniques for wholep ..."
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Profiling can accurately analyze program behavior for select data inputs. This article shows that profiling can also predict program locality for inputs other than profiled ones. Here locality is defined by the distance of data reuse. The article describes three distancebased techniques for whole
Mining distancebased outliers from large databases in any metric space
 IN: KDD
, 2006
"... Let R be a set of objects. An object o ∈ R is an outlier, if there exist less than k objects in R whose distances to o are at most r. The values of k, r, and the distance metric are provided by a user at the run time. The objective is to return all outliers with the smallest I/O cost. This paper con ..."
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Cited by 22 (0 self)
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Let R be a set of objects. An object o ∈ R is an outlier, if there exist less than k objects in R whose distances to o are at most r. The values of k, r, and the distance metric are provided by a user at the run time. The objective is to return all outliers with the smallest I/O cost. This paper
A vertical distancebased outlier detection method with local pruning
 In CAINE
, 2004
"... “One person’s noise is another person’s signal”. Outlier detection is used to clean up datasets and also to discover useful anomalies, such as criminal activities in electronic commerce, computer intrusion attacks, terrorist threats, agricultural pest infestations, etc. Thus, outlier detection is cr ..."
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Cited by 7 (0 self)
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structures, is adopted to facilitate efficient outlier detection further. We tested our methods against national hockey league dataset and show an order of magnitude of speed improvement compared to the contemporary distancebased outlier detection approaches.
DistanceBased HighFrequency Trading
"... The present paper approaches highfrequency trading from a computational science perspective, presenting a pattern recognition model to predict price changes of stock market assets. The technique is based on the featureweighted Euclidean distance to the centroid of a training cluster. A set of micr ..."
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Cited by 1 (1 self)
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The present paper approaches highfrequency trading from a computational science perspective, presenting a pattern recognition model to predict price changes of stock market assets. The technique is based on the featureweighted Euclidean distance to the centroid of a training cluster. A set
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