Effects of Inter-agent Communication in Ant-Based Clustering Algorithms: A Case Study on Communication Policies in Swarm Systems

2005 ◽  
pp. 254-263 ◽  
Author(s):  
Marco Antonio Montes de Oca ◽  
Leonardo Garrido ◽  
José Luis Aguirre
Keyword(s):  
Clustering Algorithms ◽  
Agent Communication ◽  
Communication Policies

scholarly journals Spatial analysis of urban material stock with clustering algorithms: A Northern European case study

2019 ◽  
Vol 23 (6) ◽  
pp. 1328-1343
Author(s):  
Paul Gontia ◽  
Liane Thuvander ◽  
Babak Ebrahimi ◽  
Victor Vinas ◽  
Leonardo Rosado ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Clustering Algorithms ◽  
Northern European

Innovation Problem Identification and Decision-Making for Product Platform

2009 ◽  
Vol 628-629 ◽  
pp. 131-136 ◽  
Author(s):  
Yu Yi Liu ◽  
Liang Hou ◽  
Hong Lian Wang
Keyword(s):  
Decision Making ◽  
Clustering Algorithms ◽  
Value Engineering ◽  
Product Platform ◽  
Problem Identification ◽  
Existing Problems ◽  
Correlation Degree ◽  
Production Capacities ◽  
Platform Evolution

During product platform life cycle, innovation problem identification and decision-making are regarded as vital issues in product platform evolution process. The Comprehensive Disturbance Degree is proposed and analyzed considering customer demand, technology status and production capacities, then existing problems of product platform are identified. According to Innovation Problem Selecting Principles, the innovation problem set Q is defined. The value of modules in product platform is calculated using Value Engineering, the module set M needed to be improved is determined. Then based upon the correlation degree analysis of the innovation problem set and the module set, Fuzzy Clustering Algorithms is advanced to classify innovation problems. Finally, a case study is given to illustrate the validity of the methodology.

scholarly journals Space–time series clustering: Algorithms, taxonomy, and case study on urban smart cities

2020 ◽  
Vol 95 ◽  
pp. 103857
Author(s):  
Asma Belhadi ◽  
Youcef Djenouri ◽  
Kjetil Nørvåg ◽  
Heri Ramampiaro ◽  
Florent Masseglia ◽  
...  
Keyword(s):  
Time Series ◽  
Smart Cities ◽  
Clustering Algorithms ◽  
Space Time ◽  
Time Series Clustering

scholarly journals Evolutionary dataset optimisation: learning algorithm quality through evolution

2019 ◽  
Vol 50 (4) ◽  
pp. 1172-1191 ◽  
Author(s):  
Henry Wilde ◽  
Vincent Knight ◽  
Jonathan Gillard
Keyword(s):  
Finite Number ◽  
Open Source ◽  
Learning Algorithm ◽  
Clustering Algorithms ◽  
Genetic Evolution ◽  
Artificial Data ◽  
New Approach ◽  
Benchmark Datasets ◽  
Novel Method

AbstractIn this paper we propose a novel method for learning how algorithms perform. Classically, algorithms are compared on a finite number of existing (or newly simulated) benchmark datasets based on some fixed metrics. The algorithm(s) with the smallest value of this metric are chosen to be the ‘best performing’. We offer a new approach to flip this paradigm. We instead aim to gain a richer picture of the performance of an algorithm by generating artificial data through genetic evolution, the purpose of which is to create populations of datasets for which a particular algorithm performs well on a given metric. These datasets can be studied so as to learn what attributes lead to a particular progression of a given algorithm. Following a detailed description of the algorithm as well as a brief description of an open source implementation, a case study in clustering is presented. This case study demonstrates the performance and nuances of the method which we call Evolutionary Dataset Optimisation. In this study, a number of known properties about preferable datasets for the clustering algorithms known as k-means and DBSCAN are realised in the generated datasets.

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