{"product_id":"9781484233146","title":"Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications","description":"\u003ch1\u003eData Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications\u003c\/h1\u003e \u003ch2\u003eMasters, Timothy\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cdiv\u003eDiscover hidden relationships among the variables in your data, and learn how to exploit these relationships.  This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications.  All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code.\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003eMany of these techniques are recent developments, still not in widespread use.  Others are standard algorithms given a fresh look.  In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program.  The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work.\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cb\u003eWhat You'll Learn\u003c\/b\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cdiv\u003e\u003cul\u003e\n\u003cli\u003eUse Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eDiscover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eWork with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eSee how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003ePlot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cb\u003e\u003cbr\u003e\u003c\/b\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003cbr\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003eAnyone interested in discovering and exploiting relationships among variables.  Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.\u003c\/div\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Apress\u003c\/p\u003e \u003cp\u003ePublication Date: 2017-12-19\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9781484233146\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-1-4842-3315-3\u003c\/p\u003e \u003cp\u003eDimensions: 254cm x178cm\u003c\/p\u003e \u003cp\u003ePages: 286\u003c\/p\u003e ","brand":"Apress","offers":[{"title":"Default Title","offer_id":47524786733196,"sku":"9781484233146","price":71.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781484233146.jpg?v=1776019440","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9781484233146","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}