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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