A two-level clustering method using linear linkage encoding

Linear Linkage Encoding (LLE) is a representational scheme proposed for Genetic Algorithms (GA). LLE is convenient to be used for grouping problems and it doesn't suffer from the redundancy problem that exists in classical encoding schemes. Any number of groups can be represented in a fixed len...

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Main Author: Korkmaz, E.E.
Other Authors: Yeditepe Üniversitesi
Format: Conference Object
Language:English
Published: Springer Verlag 2020
Subjects:
Online Access:https://hdl.handle.net/20.500.11831/1140
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spelling ftyeditepeuniv:oai:openaccess.yeditepe.edu.tr:20.500.11831/1140 2023-08-20T04:07:29+02:00 A two-level clustering method using linear linkage encoding Korkmaz, E.E. Korkmaz, E.E. Yeditepe Üniversitesi 2020-03-17 https://hdl.handle.net/20.500.11831/1140 eng eng Springer Verlag Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı 681 690 4193 LNCS 03029743 3540389903; 9783540389903 https://hdl.handle.net/20.500.11831/1140 info:eu-repo/semantics/closedAccess conferenceObject 2020 ftyeditepeuniv https://doi.org/20.500.11831/1140 2023-07-28T09:01:18Z Linear Linkage Encoding (LLE) is a representational scheme proposed for Genetic Algorithms (GA). LLE is convenient to be used for grouping problems and it doesn't suffer from the redundancy problem that exists in classical encoding schemes. Any number of groups can be represented in a fixed length chromosome in this scheme. However, the length of the chromosome in LLE is determined by the number of elements to be grouped just like the other encoding schemes. This disadvantage becomes dominant when LLE is applied on large datasets and the encoding turns out to be an infeasible model. In this paper a twolevel approach is proposed for LLE in order to overcome the problem. In this method, the large dataset is divided into a group of subsets. In the first phase of the process, the data in the subsets are grouped using LLE. Then these groups are used to obtain the final partitioning of the data in the second phase. The approach is tested on the clustering problem. Two considerably large datasets have been chosen for the experiments. It is not possible to obtain a satisfactory convergence with the straightforward application of LLE on these datasets. The method proposed can cluster the datasets with low error rates. © Springer-Verlag Berlin Heidelberg 2006. University of Iceland 9th International Conference on Parallel Problem Solving from Nature, PPSN IX -- 9 September 2006 through 13 September 2006 -- Reykjavik -- 68384 Conference Object Iceland Yeditepe University Institutional Repository
institution Open Polar
collection Yeditepe University Institutional Repository
op_collection_id ftyeditepeuniv
language English
description Linear Linkage Encoding (LLE) is a representational scheme proposed for Genetic Algorithms (GA). LLE is convenient to be used for grouping problems and it doesn't suffer from the redundancy problem that exists in classical encoding schemes. Any number of groups can be represented in a fixed length chromosome in this scheme. However, the length of the chromosome in LLE is determined by the number of elements to be grouped just like the other encoding schemes. This disadvantage becomes dominant when LLE is applied on large datasets and the encoding turns out to be an infeasible model. In this paper a twolevel approach is proposed for LLE in order to overcome the problem. In this method, the large dataset is divided into a group of subsets. In the first phase of the process, the data in the subsets are grouped using LLE. Then these groups are used to obtain the final partitioning of the data in the second phase. The approach is tested on the clustering problem. Two considerably large datasets have been chosen for the experiments. It is not possible to obtain a satisfactory convergence with the straightforward application of LLE on these datasets. The method proposed can cluster the datasets with low error rates. © Springer-Verlag Berlin Heidelberg 2006. University of Iceland 9th International Conference on Parallel Problem Solving from Nature, PPSN IX -- 9 September 2006 through 13 September 2006 -- Reykjavik -- 68384
author2 Korkmaz, E.E.
Yeditepe Üniversitesi
format Conference Object
author Korkmaz, E.E.
spellingShingle Korkmaz, E.E.
A two-level clustering method using linear linkage encoding
author_facet Korkmaz, E.E.
author_sort Korkmaz, E.E.
title A two-level clustering method using linear linkage encoding
title_short A two-level clustering method using linear linkage encoding
title_full A two-level clustering method using linear linkage encoding
title_fullStr A two-level clustering method using linear linkage encoding
title_full_unstemmed A two-level clustering method using linear linkage encoding
title_sort two-level clustering method using linear linkage encoding
publisher Springer Verlag
publishDate 2020
url https://hdl.handle.net/20.500.11831/1140
genre Iceland
genre_facet Iceland
op_relation Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
681
690
4193 LNCS
03029743
3540389903; 9783540389903
https://hdl.handle.net/20.500.11831/1140
op_rights info:eu-repo/semantics/closedAccess
op_doi https://doi.org/20.500.11831/1140
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