tag:crantastic.org,2005:/packages/subgroup-discoveryLatest activity for subgroup.discovery2019-06-21T16:43:21Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/898322019-06-21T16:43:21Z2019-06-21T16:43:21Zsubgroup.discovery was upgraded to version 0.2.1<a href="/packages/subgroup-discovery">subgroup.discovery</a> was <span class="action">upgraded</span> to version <a href="/packages/subgroup-discovery/versions/85422">0.2.1</a><br /><h3>Package description:</h3><p>Developed to assist in discovering interesting subgroups in high-dimensional data. The PRIM implementation is based on the 1998 paper "Bump hunting in high-dimensional data" by Jerome H. Friedman and Nicholas I. Fisher. <doi:10.1023/A:1008894516817> PRIM involves finding a set of "rules" which combined imply unusually large (or small) values of some other target variable. Specifically one tries to find a set of sub regions in which the target variable is substantially larger than overall mean. The objective of bump hunting in general is to find regions in the input (attribute/feature) space with relatively high (low) values for the target variable. The regions are described by simple rules of the type if: condition-1 and ... and condition-n then: estimated target value. Given the data (or a subset of the data), the goal is to produce a box B within which the target mean is as large as possible. There are many problems where finding such regions is of considerable practical interest. Often these are problems where a decision maker can in a sense choose or select the values of the input variables so as to optimize the value of the target variable. In bump hunting it is customary to follow a so-called covering strategy. This means that the same box construction (rule induction) algorithm is applied sequentially to subsets of the data.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/650982017-08-02T09:22:29Z2017-08-02T09:22:29Zsubgroup.discovery was upgraded to version 0.2.0<a href="/packages/subgroup-discovery">subgroup.discovery</a> was <span class="action">upgraded</span> to version <a href="/packages/subgroup-discovery/versions/62260">0.2.0</a><br /><h3>Package description:</h3><p>Developed to assist in discovering interesting subgroups in high-dimensional data. The PRIM implementation is based on the 1998 paper "Bump hunting in high-dimensional data" by Jerome H. Friedman and Nicholas I. Fisher. <doi:10.1023/A:1008894516817> PRIM involves finding a set of "rules" which combined imply unusually large (or small) values of some other target variable. Specifically one tries to find a set of sub regions in which the target variable is substantially larger than overall mean. The objective of bump hunting in general is to find regions in the input (attribute/feature) space with relatively high (low) values for the target variable. The regions are described by simple rules of the type if: condition-1 and ... and condition-n then: estimated target value. Given the data (or a subset of the data), the goal is to produce a box B within which the target mean is as large as possible. There are many problems where finding such regions is of considerable practical interest. Often these are problems where a decision maker can in a sense choose or select the values of the input variables so as to optimize the value of the target variable. In bump hunting it is customary to follow a so-called covering strategy. This means that the same box construction (rule induction) algorithm is applied sequentially to subsets of the data.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/647362017-07-20T12:41:56Z2017-07-20T12:41:56Zsubgroup.discovery was released<a href="/packages/subgroup-discovery">subgroup.discovery</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Developed to assist in discovering interesting subgroups in high-dimensional data. The PRIM implementation is based on the 1998 paper "Bump hunting in high-dimensional data" by Jerome H. Friedman and Nicholas I. Fisher. <doi:10.1023/A:1008894516817> PRIM involves finding a set of "rules" which combined imply unusually large (or small) values of some other target variable. Specifically one tries to find a set of sub regions in which the target variable is substantially larger than overall mean. The objective of bump hunting in general is to find regions in the input (attribute/feature) space with relatively high (low) values for the target variable. The regions are described by simple rules of the type if: condition-1 and ... and condition-n then: estimated target value. Given the data (or a subset of the data), the goal is to produce a box B within which the target mean is as large as possible. There are many problems where finding such regions is of considerable practical interest. Often these are problems where a decision maker can in a sense choose or select the values of the input variables so as to optimize the value of the target variable. In bump hunting it is customary to follow a so-called covering strategy. This means that the same box construction (rule induction) algorithm is applied sequentially to subsets of the data.</p>crantastic.org