I am going to try out a tentative clustering of some feature vectors.
The range of values spanned by the three items making up the features vector is
quite different:
Item-1 goes roughly from 70 to 525 (integer numbers only)
Item-2 is in-between 0 and 1 (all real numbers between 0 and 1)
Item-3 goes from 1 to 10 (integer numbers only)
In order to spread out Item-2 even further I might try to replace Item-2 with
Log10(Item-2).
My concern is that, regardless the distance measure used, the item whose order
of magnitude is the highest may carry the highest weight in the process of
calculating the similarity matrix therefore fading out the influence of the
items with smaller variation in the resulting clusters.
Should I normalize all feature vector elements to 1 in advance of generating
the similarity matrix ?
Thank you so much.
Maura
tutti i telefonini TIM!
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