【論】Xing,2001,CLIFF: clustering of high-demensiona～

Eric P.Xing and Richard M.Karp
CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts
Bioinformatics Vol. 17 no. 90001 2001 Pages S306-S315
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・マイクロアレイデータのクラス分け法の提案。
・データ：白血病, 72サンプル(47 ALL/ 25 AML), 7130遺伝子 [Golub]
・比較したクラス分け法
1. Approximate NCut without feature selection
2. K-means (for K=2) without feature selection
3. CLIFF (Approximate NCut with iterative feature selection and partitioning)
4. K-means (for K=2) with feature selection

・問題点「This clustering problem is difficult for several reasons, in particular the sparsity of the data, the high demensionality of the feature (gene) space, and the fact that many features are irrelevant or redundant.」
・CLIFFとは「In this paper, we propose a novel algorithm, CLIFF (Clustering via Iterative Feature Filtering), which combines a clustering process and a feature selection process in a bootstrap-like iterative way, where each process uses the output of the other as an approximate input, and the outputs of the two processes improve hand-in-hand over the course of the iterations.」