{"product_id":"instance-selection-and-construction-for-data-mining-von-huan-liu-hiroshi-motoda-hrsg","title":"Instance Selection and Construction for Data Mining","description":"The ability to analyze and understand massive data sets lags  far behind the ability to gather and store the data. To meet this  challenge, knowledge discovery and data mining (KDD) is growing  rapidly as an emerging field. However, no matter how powerful  computers are now or will be in the future, KDD researchers and  practitioners must consider how to manage ever-growing data which is,  ironically, due to the extensive use of computers and ease of data  collection with computers. Many different approaches have been used to  address the data explosion issue, such as algorithm scale-up and data  reduction. Instance, example, or tuple selection pertains to methods  or algorithms that select or search for a representative portion of  data that can fulfill a KDD task as if the whole data is used.  Instance selection is directly related to data reduction and becomes  increasingly important in many KDD applications due to the need for  processing efficiency and\/or storage efficiency. \u003cbr\u003e  One of the major means of instance selection is sampling whereby a  sample is selected for testing and analysis, and randomness is a key  element in the process. Instance selection also covers methods that  require search. Examples can be found in density estimation (finding  the representative instances - data points - for a  cluster); boundary hunting (finding the critical instances to form  boundaries to differentiate data points of different classes); and  data squashing (producing weighted new data with equivalent sufficient  statistics). Other important issues related to instance selection  extend to unwanted precision, focusing, concept drifts, noise\/outlier  removal, data smoothing, etc. \u003cbr\u003e  \u003cem\u003eInstance Selection and Construction for Data Mining\u003c\/em\u003e brings  researchers and practitioners together to report new developments and  applications, to share hard-learned experiences in order to avoid  similar pitfalls, and to shed light on the future development of  instance selection. This volume serves as a comprehensive reference  for graduate students, practitioners and researchers in KDD.\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9781441948618\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9780792372097\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Softcover - 9781441948618","offer_id":39415611261021,"sku":"9781441948618","price":160.49,"currency_code":"EUR","in_stock":true},{"title":"Hardcover - 9780792372097","offer_id":50828678406,"sku":"9780792372097","price":160.49,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/77401fa8-6e79-461f-a1fc-1977fbfdd424.jpg?v=1766639127","url":"https:\/\/shop.autorenwelt.de\/en\/products\/instance-selection-and-construction-for-data-mining-von-huan-liu-hiroshi-motoda-hrsg","provider":"Autorenwelt Shop","version":"1.0","type":"link"}