Kniha Clustering for Classification Reuben Evans

Clustering for Classification

Using Standard Clustering Methods to Summarise Datasets with Minimal Loss of Classification Accuracy

Autor: Reuben Evans
Jazyk: Angličtina
Vazba: Brožovaná
Dostupnost: Skladem u dodavatele
Odesíláme za 14-21 dnů
1 149
Advances in technology have provided industry with an arrayof devices for collecting data. The frequ...

Informace o knize

Autor
Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2008
Stránek
108
EAN
9783639031638
ISBN
3639031636
Enbook ID
06812870
Hmotnost
154
Rozměry
152 x 229 x 6

Kompletní popis

Advances in technology have provided industry with an arrayof devices for collecting data. The frequency and scale of data collection means that there are now many large datasets being generated. To find patterns in these datasets it would be useful to be able to apply modern methods of classification such as support vector machines. Unfortunately these methods are computationally expensive, quadratic in the number of data points in fact, and socannot be applied directly. This book proposes a framework where by a variety of clustering methods can be used to summarise datasets, that is, reduce them to a smaller but still representative dataset so that these advanced methods can be applied. It compares the results of using this framework against using random selection on alarge number of classification and regression problems. Results show thatthe clustered datasets are on average fifty percent smaller than the original datasets without loss of classification accuracy which is significantly better than random selection. They also show that there is no free lunch, for each dataset it is important to choose a clustering method carefully.

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