A Knowledge Discovery Method for Characteristics Extraction of Power Consumption Based on Cluster Analysis

2019 ◽  
Author(s):  
Daoqu Geng ◽  
Xue Xia ◽  
Xinshuai Fu
Keyword(s):  
Cluster Analysis ◽  
Power Consumption ◽  
Knowledge Discovery ◽  
Discovery Method

Application of cluster analysis for enhancing power consumption awareness in smart grids

2018 ◽  
pp. 397-414
Author(s):  
Guido Coletta ◽  
Alfredo Vaccaro ◽  
Domenico Villacci ◽  
Ahmed F. Zobaa
Keyword(s):  
Cluster Analysis ◽  
Power Consumption ◽  
Smart Grids

Cluster Analysis as a First Step in the Knowledge Discovery Process

2000 ◽  
Vol 4 (4) ◽  
pp. 258-262 ◽  
Author(s):  
Andreas Rauber ◽  
◽  
Jan Paralic ◽  
Keyword(s):  
Cluster Analysis ◽  
Knowledge Discovery ◽  
Unsupervised Classification ◽  
Lessons Learned ◽  
Self Organizing Map ◽  
Discovery Process ◽  
Ticket Sales ◽  
Starting Point ◽  
Unsupervised Neural Network ◽  
Insight Into

Cluster analysis is one of the most prominent methods for the analysis of large, unknown datasets. It provides a particularly suitable tool for obtaining a first overview of data, forming a prominent starting point for further evaluation. . In this paper, we present some lessons learned during the application of two clustering approaches to the analysis of castle admission ticket sales data. A Bayesian unsupervised classification based on AutoClass and an unsupervised neural network, the Self-Organizing Map, are used to obtain a first impression of the available data to form the basis for further exploration. We show that this type of cluster analysis provides a suitable first step in the knowledge discovery process. The different types of result representation and their suitability of providing a first insight into datasets are analyzed and compared.

scholarly journals Comparing and Clustering Residential Layouts Using a Novel Measure of Grating Difference

2020 ◽  
Author(s):  
Ran Xiao
Keyword(s):  
Knowledge Discovery ◽  
Design Patterns ◽  
Topological Properties ◽  
Clustering Method ◽  
Geometrical Properties ◽  
Difference Measure ◽  
Discovery Method ◽  
Architectural Research

Abstract Clustering is widely used as a knowledge discovery method in scientific studies but is not often used in architectural research. This paper applies clustering to a dataset of 129 residential layouts, which were collected from contemporary architectural practices, to reveal underlying design patterns. To achieve this, this paper introduces a novel measure for the topological properties of layouts: ‘grating difference measure’. It was benchmarked against an alternative that measures geometrical properties and the advantages are explained. The grating difference measure indicates the extent of design differences, which is used in the clustering method to obtain the distance between datapoints. The results from clustering were grouped into design schematics and qualitatively assessed, showing a convincing separation of characteristics. The method demonstrated in this paper may be used to reveal topological patterns in datasets of existing designs for both academic and practical purposes.

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