[PDF] Top 20 Detection of outliers in multivariate data: a method based on clustering and robust estimators
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Detection of outliers in multivariate data: a method based on clustering and robust estimators
... important in many applications of multivariate anal- ...interest in finding anomalous ob- servations or as a pre-processing task before the application of some multi- variate ... See full document
6
Least squares image matching: A comparison of the performance of robust estimators
... Multiple outliers rather than one single outlier are in existence with a higher possibility, and it is well known that the estimation in LSM is essentially least squares estimation, which is ... See full document
8
Survey of Clustering based Financial Fraud Detection Research
... rules, data warehouse and Bayes ...fraud detection system consisting of 4 components: rule-based filter, Dempster –Shafer adder, transaction history database, Bayesian ...rule- ... See full document
13
Wavelet-Based Detection of Outliers in Poisson INAR(1) Time Series
... other data, may contain outliers which are observations that look discordant from most of the observations in the ...presence of outliers in a time series hinders ... See full document
12
A robust method for inverse transport modeling of atmospheric emissions using blind outlier detection
... Emissions of harmful substances into the atmo- sphere are a serious environmental ...concern. In order to un- derstand and predict their effects, it is necessary to estimate the exact quantity ... See full document
9
Detection of outliers and outliers clustering on large datasets with distributed computing
... objects and tries to iden- tify those more distant from the ...most of the concepts of distribution-based and depth-based algorithms, but with a better computa- tional ... See full document
190
Automatic detection of discordant outliers via the Ueda's method
... aim of this paper is to study the performance and robustness of the Ueda’s method to detect outliers via computer simulations, as well as determine its applica- bility to other types ... See full document
14
AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA
... partitional clustering algorithm in the remote sensing ...placement of cluster ...high-dimensional data such as hyperspectral remotely sensed ...problem, in this paper, the spectral ... See full document
5
Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s Self-Organizing Map
... GEO3DSOM on the other hand outperforms the stan- dard SOM in providing a grouping of the data in a spatially coherent ...Analysis of both the artificial and the real life ... See full document
13
COMBINATION OF DIFFERENCE SUBSPACE AND OPPURTUNISTIC CLUSTERING ON HIGH DIMENSIONAL DATA
... analyzing data using the real datasets, certain problems related to outlying are ...work of Rousseeuw and Hubert (2011) aimed at detecting the attackers by searching for the model using the majority ... See full document
8
A Robust Modification of the Goldfeld-Quandt Test for the Detection of Heteroscedasticity in the Presence of Outliers
... test of heteroscedasticity proposed by White [20] does not rely on the normality assumption and is very easy to ...illustration of the basic idea, consider the following three-variable ... See full document
7
Outlier detection for improved clustering : empirical research for unsupervised data mining
... point’ and k-NN parameter. Returning to figure 14, three observations in the right plot are identified with 𝑀𝐷𝐸𝐹 ≈ 1, indicating neighboring observations in average have twice as dense ...as ... See full document
171
Robust clustering method for the detection of outliers: using AIC to select the number of clusters
... One of the difficulties encountered in the implementation of the method was the choice of the number of clusters, k, as well as the clustering method and the ... See full document
7
Robust Clustering Method for the Detection of Outliers: Using AIC to Select the Number of Clusters
... multivariate data based on clustering and robust estimators was introduced in ...similar method designed to work with non-overlapping clusters was ... See full document
8
Detection of Outliers in Multivariate Data: A Method Based on Influence Eigen
... subset of observations) that is isolated from the other observations in the data ...find outliers; the first is the researchers ...effects of an outlier on analy- ...outlier ... See full document
5
Detection of outliers for a pharmaceutical distribution company in Portugal | Deteção de outliers numa empresa de distribuição farmacêutica em Portugal
... use of the time series data mining technique for the sales prediction of individual products of a pharmaceutical distribution company in ...Through data mining techniques, the ... See full document
7
Design parameter based method of partial discharge detection and location in power transformers
... PD detection method is based on the fact that partial discharges produce acoustic waves in a frequency range up to 150 kHz within the transformer tank ...placed on the outer ... See full document
13
Spatio-temporal data mining in palaeogeographic data with a density-based clustering algorithm
... usefulness of data mining and the process of Knowledge Discovery in Databases (KDD) has increased its importance as grows the volume of data stored in large ... See full document
103
Face Detection Method Based on Color Barycenter Hexagon Model
... result in complex background with 3 ...thresholding and the effect is good for vision, but not fit to detection the face; ...result of CBH model and ...CBH method. For ... See full document
4
Link Spam Detection Based on DBSPAMCLUST with Fuzzy C-Means Clustering
... the detection of the content quality of web pages is a time consuming ...use of topical information to partition the seed set and calculate trust scores for each topic separately, ... See full document
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