Data Clustering Using Grouping Hyper-heuristics

Evolutionary Computation in Combinatorial Optimization - Lecture Notes in Computer Science ◽

10.1007/978-3-319-77449-7_7 ◽

2018 ◽

pp. 101-115 ◽

Cited By ~ 1

Author(s):

Anas Elhag ◽

Ender Özcan

Keyword(s):

Data Clustering

Download Full-text

Framework to Analyze Construction Labor Productivity Using Fuzzy Data Clustering and Multi-Criteria Decision-Making

Construction Research Congress 2020 ◽

10.1061/9780784482865.006 ◽

2020 ◽

Author(s):

Matin Kazerooni ◽

Mohammad Raoufi ◽

Aminah Robinson Fayek

Keyword(s):

Decision Making ◽

Labor Productivity ◽

Data Clustering ◽

Fuzzy Data ◽

Multi Criteria Decision Making

Download Full-text

Balanced Data Clustering Algorithm for Both Hard and Soft Clustering

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i2.176183 ◽

2018 ◽

Vol 6 (2) ◽

pp. 176-183

Author(s):

Purnendu Das ◽

◽

Bishwa Ranjan Roy ◽

Saptarshi Paul ◽

◽

...

Keyword(s):

Data Clustering ◽

Clustering Algorithm ◽

Soft Clustering

Download Full-text

DATA CLUSTERING DURING A GEOLOGICAL-FIELD ANALYSIS

Oilfield Engineering ◽

10.30713/0207-2351-2020-10(622)-49-52 ◽

2020 ◽

pp. 49-52

Author(s):

M.R. Dulkarnaev ◽

◽

R.R. Yunusov ◽

I.V. Ryabov ◽

P.Yu. Lobanov ◽

...

Keyword(s):

Data Clustering ◽

Field Analysis

Download Full-text

WSN Localization Method Using Interval Data Clustering

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2012.01190 ◽

2012 ◽

Vol 38 (7) ◽

pp. 1190 ◽

Cited By ~ 7

Author(s):

Yu PENG ◽

Qing-Hua LUO ◽

Dan WANG ◽

Xi-Yuan PENG

Keyword(s):

Data Clustering ◽

Interval Data ◽

Localization Method

Download Full-text

Network performance data clustering method based on semantic description and optimization

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.01522 ◽

2013 ◽

Vol 32 (6) ◽

pp. 1522-1525

Author(s):

Da-qing JIANG ◽

Yong ZHOU ◽

Shi-xiong XIA

Keyword(s):

Data Clustering ◽

Network Performance ◽

Performance Data ◽

Semantic Description ◽

Clustering Method

Download Full-text

IsoClustering: A Generalized Framework for Local Data Clustering

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) ◽

10.1109/icmla.2019.00058 ◽

2019 ◽

Author(s):

David Haley ◽

Ehsan Kamalinejad ◽

Jiaofei Zhong

Keyword(s):

Data Clustering ◽

Local Data ◽

Generalized Framework

Download Full-text

Cost-Benefit Evaluation for Battery Energy Storage Considering Degradation and Data Clustering in Performance-Based Frequency Regulation Service

2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2) ◽

10.1109/ei250167.2020.9346714 ◽

2020 ◽

Author(s):

Xueling Zheng ◽

Ye Su ◽

Luping Wei ◽

Jiangfeng Zhang ◽

Xinwei Shen ◽

...

Keyword(s):

Energy Storage ◽

Data Clustering ◽

Cost Benefit ◽

Frequency Regulation ◽

Battery Energy Storage ◽

Benefit Evaluation ◽

Battery Energy

Download Full-text

A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Mathematics ◽

10.3390/math9070786 ◽

2021 ◽

Vol 9 (7) ◽

pp. 786

Author(s):

Yenny Villuendas-Rey ◽

Eley Barroso-Cubas ◽

Oscar Camacho-Nieto ◽

Cornelio Yáñez-Márquez

Keyword(s):

Swarm Intelligence ◽

Data Clustering ◽

Incomplete Data ◽

Missing Values ◽

Clustering Algorithms ◽

Bat Algorithm ◽

Hybrid Features ◽

Bee Colony ◽

Learning Tasks ◽

Clustering Data

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

Download Full-text