Feature weighting for clustering

Feature weighting for clustering


Marketed By :  LAP LAMBERT Academic Publishing   Sold By :  Kamal Books International  
Delivery in :  10-12 Business Days

₹ 5,066

Availability: Out of stock


Delivery :

5% Cashback on all Orders paid using MobiKwik Wallet T&C

Free Krispy Kreme Voucher on all Orders paid using UltraCash Wallet T&C
Product Out of Stock Subscription

(Notify me when this product is back in stock)

  • Product Description

K-Means is arguably the most popular clustering algorithm; this is why it is of great interest to tackle its shortcomings. The drawback in the heart of this project is that this algorithm gives the same level of relevance to all the features in a dataset. This can have disastrous consequences when the features are taken from a database just because they are available. To address the issue of unequal relevance of the features we use a three-stage extension of the generic K-Means in which a third step is added to the usual two steps in a K-Means iteration: feature weighting update. We extend the generic K-Means to what we refer to as Minkowski Weighted K-Means method. We apply the developed approaches to problems in distinguishing between different mental tasks over high-dimensional EEG data.

Product Specifications
SKU :COC18149
Country of ManufactureIndia
Product BrandLAP LAMBERT Academic Publishing
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-07-27
0 Review(s)