Mining imperfect data: dealing with contamination and incomplete records
Ronald K. Pearson (Society for Industrial and Applied Mathematics, 2005)
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| Data mining is concerned with the analysis of databases large enough that various anomalies, including outliers, incomplete data records, and more subtle phenomena such as misalignment errors, are virtually certain to be present. Mining Imperfect Data describes in detail a number of these problems, as well as their sources, their consequences, their detection, and their treatment. Specific strategies for data pretreatment and analytical validation that are broadly applicable are described, making them useful in conjunction with most data mining analysis methods. Examples are presented to illustrate the performance of the pretreatment and validation methods in a variety of situations, both simulation based, where "correct" results are known unambiguously, and real data examples that illustrate typical cases met in practice. |
 Mining imperfect data dealing with contamination and incomplete records.pdf :: Unduh
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| No. Panggil : | e20443143 |
| Entri utama-Nama orang : | |
| Subjek : | |
| Penerbitan : | Philadelphia : Society for Industrial and Applied Mathematics, 2005 |
| Sumber Pengatalogan: | LibUI eng rda |
| Tipe Konten: | text |
| Tipe Media: | computer |
| Tipe Pembawa: | online resource |
| Deskripsi Fisik: | x, 305 pages : illustration |
| Tautan: | http://portal.igpublish.com/iglibrary/search/SIAMB0000039.main.html?21 |
| Lembaga Pemilik: | |
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| No. Panggil | No. Barkod | Ketersediaan |
|---|---|---|
| e20443143 | TERSEDIA |
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| Tidak ada ulasan pada koleksi ini: 20443143 |
