{"product_id":"fractal-clustering-von-ping-chen-und-daniel-barbara","title":"FRACTAL CLUSTERING","description":"\u003cp\u003eClustering is a widely used knowledge discovery  technique. Large-scale clustering has received a lot  of attention recently. However, existing algorithms often do not scale with the size  of the data and the number of dimensions, or fail to  find arbitrary shapes of clusters or deal  effectively with the presence of noise. In this book  a new clustering algorithm based on self-similarity  properties is discussed. Self-similarity is the  property of being invariant with respect to the  scale used to look at the data set. While fractals  are self-similar at every scale, many data sets only  exhibit self-similarity over a range of scales. Self- similarity can be measured using the fractal  dimension. Our new clustering algorithm called  Fractal Clustering (FC) places points incrementally  in the cluster for which the change in the fractal  dimension after adding the point is the least, so  points in the same cluster have a great degree of  self-similarity among them (and much less self- similarity with respect to points in other  clusters). Two applications on projected clustering  and tracking deviation in evolving data sets are  also discussed.\u003c\/p\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783843362122\"\u003e\u003ch3\u003eITS APPLICATIONS ON PROJECTED CLUSTERING AND TREND ANALYSIS\u003c\/h3\u003e\u003c\/div\u003e","brand":"Autorenwelt Shop","offers":[{"title":"Softcover - 9783843362122","offer_id":39497112092765,"sku":"9783843362122","price":59.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/0b946740-6561-41e0-9098-397ba2501ee1.jpg?v=1772948971","url":"https:\/\/shop.autorenwelt.de\/en\/products\/fractal-clustering-von-ping-chen-und-daniel-barbara","provider":"Autorenwelt Shop","version":"1.0","type":"link"}