Monitoring weather based meteorological data: Clustering approach for analysis

2017 ◽  
Author(s):  
N Shobha ◽  
T. Asha
Keyword(s):  
Data Clustering ◽  
Meteorological Data ◽  
Clustering Approach

Featureless Data Clustering

2010 ◽  
pp. 141-164 ◽  
Author(s):  
Wilson Wong
Keyword(s):  
Data Clustering ◽  
Dominant Role ◽  
Clustering Algorithms ◽  
Adaptive Clustering ◽  
Feature Based ◽  
Clustering Approach ◽  
Semantic Computation ◽  
The Many ◽  
Time Required ◽  
Existing Data

Feature-based semantic measurements have played a dominant role in conventional data clustering algorithms for many existing applications. However, the applicability of existing data clustering approaches to a wider range of applications is limited due to issues such as complexity involved in semantic computation, long pre-processing time required for feature preparation, and poor extensibility of semantic measurement due to non-incremental feature source. This chapter first summarises the many commonly used clustering algorithms and feature-based semantic measurements, and then highlights the shortcomings to make way for the proposal of an adaptive clustering approach based on featureless semantic measurements. The chapter concludes with experiments demonstrating the performance and wide applicability of the proposed clustering approach.

Herd Clustering: A synergistic data clustering approach using collective intelligence

2014 ◽  
Vol 23 ◽  
pp. 61-75 ◽  
Author(s):  
Ka-Chun Wong ◽  
Chengbin Peng ◽  
Yue Li ◽  
Tak-Ming Chan
Keyword(s):  
Data Clustering ◽  
Collective Intelligence ◽  
Clustering Approach

scholarly journals Dynamic and Optimized Prototype Clustering for Relational Data based on Multiple Prototypes

2019 ◽  
Vol 8 (3) ◽  
pp. 5630-5634
Keyword(s):  
Artificial Intelligence ◽  
Data Clustering ◽  
Synthetic Data ◽  
Medical Data ◽  
Relational Data ◽  
Data Sets ◽  
Optimization Approach ◽  
Complex Task ◽  
Data Points ◽  
Clustering Approach

In artificial intelligence related applications such as bio-medical, bio-informatics, data clustering is an important and complex task with different situations. Prototype based clustering is the reasonable and simplicity to describe and evaluate data which can be treated as non-vertical representation of relational data. Because of Barycentric space present in prototype clustering, maintain and update the structure of the cluster with different data points is still challenging task for different data points in bio-medical relational data. So that in this paper we propose and introduce A Novel Optimized Evidential C-Medoids (NOEC) which is relates to family o prototype based clustering approach for update and proximity of medical relational data. We use Ant Colony Optimization approach to enable the services of similarity with different features for relational update cluster medical data. Perform our approach on different bio-medical related synthetic data sets. Experimental results of proposed approach give better and efficient results with comparison of different parameters in terms of accuracy and time with processing of medical relational data sets.

scholarly journals Reducing Noises in Digital Surface Inspection Using a Data Clustering Approach

2021 ◽  
Vol 54 (1) ◽  
pp. 193-198
Author(s):  
Elnaz Ghanbary Kalajahi ◽  
Mehran Mahboubkhah ◽  
Ahmad Barari
Keyword(s):  
Data Clustering ◽  
Surface Inspection ◽  
Clustering Approach

scholarly journals Loss Modeling of Large Hydrogenerators for Cost Estimation of Reactive Power Services

2021 ◽  
Author(s):  
Yannick Cyiza Karekezi ◽  
Thomas Øyvang ◽  
Jonas Kristiansen Nøland
Keyword(s):  
Cost Estimation ◽  
Data Clustering ◽  
Reactive Power ◽  
Power Level ◽  
Active Power ◽  
New Approach ◽  
Speed Up ◽  
Additional Costs ◽  
Clustering Approach ◽  
Additional Losses

A new approach to predict the additional costs of reactive power system services delivered by large hydrogenerators is proposed in this letter. It is based on the application of the accumulated average efficiency (AAE), which has recently been proposed. An optimal operational path within the capability diagram with minimal losses is derived. This path can be used to calculate additional losses from operational regimes deviating from the optimal one for each active power level. Finally, the additional losses are accumulated in a similar manner as the AAE to estimate the extra cost of the operational regime, with ideal operation as the reference. In addition, the accuracy of a data clustering approach is explored to speed up the computation of the AAE and the accumulation of additional costs.

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