شماره ركورد كنفرانس
4214
عنوان مقاله
DCA algorithm for clusterwise linear regression and its comparison
پديدآورندگان
Taheri Sona Federation University Australia , Bagirov Adil M. Federation University Australia
تعداد صفحه
8
كليدواژه
Nonconvex optimization , DC optimization , Nonsmooth optimization , Clusterwise linear regression
سال انتشار
1396
عنوان كنفرانس
دهمين كنفرانس بين المللي تحقيق در عمليات
زبان مدرك
انگليسي
چكيده فارسي
Clusterwise linear regression consists of finding a number of linear regression functions each approximating a subset of the data. It is a combination of two techniques: clustering and regression. We introduce an algorithm for solving the clusterwise linear regression problem using its nonsmooth optimization formulation and difference of convex representation. The algorithm is tested using real world data sets and compared with other clusterwise linear regression algorithms.
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