書誌事項
- タイトル別名
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- An improved method using k-means to determine the optimal number of clusters, considering the relations between several variables
- ヘンスウカン ノ カンケイセイ オ コウリョ シテ クラスタースウ オ ケッテイ スル k meansホウ ノ カイリョウ
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抄録
In this article, we propose a non-hierarchical clustering method that can consider the relations between several variables and determine the optimal number of clusters. By utilizing the Mahalanobis distance instead of the Euclidean distance, which is calculated in k-means, we could consider the relations between several variables and obtain better groupings. Assuming that the data are samples from a mixture normal distribution, we could also calculate Akaike's information criterion (AIC) and the Bayesian information criterion (BIC) to determine the number of clusters. We used simulation and real data examples to confirm the usefulness of the proposed method. This method allows determination of the optimal number of clusters, considering the relations between several variables.
収録刊行物
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- 心理学研究
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心理学研究 82 (1), 32-40, 2011
公益社団法人 日本心理学会
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詳細情報 詳細情報について
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- CRID
- 1390001205078546048
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- NII論文ID
- 130000992467
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- NII書誌ID
- AN00123620
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- COI
- 1:STN:280:DC%2BC3MnjslyltA%3D%3D
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- ISSN
- 18841082
- 00215236
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- NDL書誌ID
- 11050922
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- PubMed
- 21706821
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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
- PubMed
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可