書誌事項
- タイトル別名
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- ISODATA clustering method with parameter estimation based on Genetic Algorithm: GA taking concaveness of probability density function into account
- カクリツ ミツド カンスウ ノ オウセイ オ コウリョシタ イデン アルゴリズム ニ モトズク パラメータ セッテイ オ トモナウ ISODATA クラスタリング
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An improved ISODATA clustering method with merge and split parameters as well as initial cluster center determination with GA: Genetic Algorithm is proposed. Although ISODATA method is well-known clustering method, there is a problem that the iteration and clustering result is strongly depending on the initial parameters, especially the threshold for merge and split. Furthermore, it shows a relatively poor clustering performance in the case that the probability density function of data in concern can not be expressed with convex function. In order to overcome this situation, GA is introduced for the determination of initial cluster center as well as the threshold of merge and split between constructing clusters. Through experiments with simulated data, the well-known UCI repository data for clustering performance evaluations and ASTER/VNIR: Visible and Near Infrared Radiometer of imagery data, the proposed method is confirmed to be superior to the conventional ISODATA method.
収録刊行物
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- 写真測量とリモートセンシング
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写真測量とリモートセンシング 47 (1), 17-25, 2008
一般社団法人 日本写真測量学会
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詳細情報 詳細情報について
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- CRID
- 1390001204077477760
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- NII論文ID
- 10024353953
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- NII書誌ID
- AN00111450
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- ISSN
- 18839061
- 02855844
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- NDL書誌ID
- 9426807
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可