scholarly journals Energy Efficient Data Mining Scheme for High Dimensional Data

2015 ◽  
Vol 46 ◽  
pp. 483-490 ◽  
Author(s):  
Mohammad Alwadi ◽  
Girija Chetty
Keyword(s):  
Data Mining ◽  
Energy Efficient ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Efficient Data ◽  
Mining Scheme

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

Aerodynamic Optimization of the Low-Pressure Turbine Module: Exploiting Surrogate Models in a High-Dimensional Design Space

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Lieven Baert ◽  
Emmanuel Chérière ◽  
Caroline Sainvitu ◽  
Ingrid Lepot ◽  
Arnaud Nouvellon ◽  
...  
Keyword(s):  
Data Mining ◽  
Design Space ◽  
Low Pressure ◽  
Surrogate Models ◽  
High Dimensional ◽  
Efficiency Gain ◽  
Aerodynamic Optimization ◽  
Component Design ◽  
Efficient Data ◽  
Lp Turbine

Abstract Further improvement of state-of-the-art low-pressure (LP) turbines (LPTs) has become progressively more challenging. LP design is more than ever confronted to the need to further integrate complex models and to shift from single-component design to the design of the complete LPT module at once. This leads to high-dimensional design spaces and automatically challenges their applicability within an industrial context, where computing resources are limited and the cycle time is crucial. The aerodynamic design of a multistage LP turbine is discussed for a design space defined by 350 parameters. Using an online surrogate-based optimization (SBO) approach, a significant efficiency gain of almost 0.5pt has been achieved. By discussing the sampling of the design space, the quality of the surrogate models, and the application of adequate data mining capabilities to steer the optimization, it is shown that despite the high-dimensional nature of the design space, the followed approach allows to obtain performance gains beyond target. The ability to control both global as well as local characteristics of the flow throughout the full LP turbine, in combination with an agile reaction of the search process after dynamically strengthening and/or enforcing new constraints in order to adapt to the review feedback, not only illustrates the feasibility but also the potential of a global design space for the LP module. It is demonstrated that intertwining the capabilities of dynamic SBO and efficient data mining allows to incorporate high-fidelity simulations in design cycle practices of certified engines or novel engine concepts to jointly optimize the multiple stages of the LPT.

Sign in / Sign up

Export Citation Format

Share Document