Opening the Black Box of Feature Extraction: Incorporating Visualization into High-Dimensional Data Mining Processes

2006 ◽  
Author(s):  
Jianting Zhang ◽  
Le Gruenwald
Keyword(s):  
Data Mining ◽  
Feature Extraction ◽  
High Dimensional Data ◽  
Black Box ◽  
High Dimensional

scholarly journals A Novel Density-based Technique for Outlier Detection of High Dimensional Data Utilizing Full Feature Space

2021 ◽  
Vol 50 (1) ◽  
pp. 138-152
Author(s):  
Mujeeb Ur Rehman ◽  
Dost Muhammad Khan
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
High Dimensional Data ◽  
Research Work ◽  
Feature Space ◽  
High Dimensional ◽  
Data Set ◽  
Data Points ◽  
Low Dimensional ◽  
Intrinsic Feature

Recently, anomaly detection has acquired a realistic response from data mining scientists as a graph of its reputation has increased smoothly in various practical domains like product marketing, fraud detection, medical diagnosis, fault detection and so many other fields. High dimensional data subjected to outlier detection poses exceptional challenges for data mining experts and it is because of natural problems of the curse of dimensionality and resemblance of distant and adjoining points. Traditional algorithms and techniques were experimented on full feature space regarding outlier detection. Customary methodologies concentrate largely on low dimensional data and hence show ineffectiveness while discovering anomalies in a data set comprised of a high number of dimensions. It becomes a very difficult and tiresome job to dig out anomalies present in high dimensional data set when all subsets of projections need to be explored. All data points in high dimensional data behave like similar observations because of its intrinsic feature i.e., the distance between observations approaches to zero as the number of dimensions extends towards infinity. This research work proposes a novel technique that explores deviation among all data points and embeds its findings inside well established density-based techniques. This is a state of art technique as it gives a new breadth of research towards resolving inherent problems of high dimensional data where outliers reside within clusters having different densities. A high dimensional dataset from UCI Machine Learning Repository is chosen to test the proposed technique and then its results are compared with that of density-based techniques to evaluate its efficiency.

scholarly journals Automatic subspace clustering of high dimensional data for data mining applications

1998 ◽  
Vol 27 (2) ◽  
pp. 94-105 ◽  
Author(s):  
Rakesh Agrawal ◽  
Johannes Gehrke ◽  
Dimitrios Gunopulos ◽  
Prabhakar Raghavan
Keyword(s):  
Data Mining ◽  
High Dimensional Data ◽  
Subspace Clustering ◽  
High Dimensional
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