確率密度関数の凹性を考慮した遺伝アルゴリズムに基づくパラメータ設定を伴うISODATAクラスタリング

書誌事項

タイトル別名
  • 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

この論文をさがす

抄録

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

参考文献 (12)*注記

もっと見る

詳細情報 詳細情報について

問題の指摘

ページトップへ