tag:crantastic.org,2005:/authors/8426Latest activity for Sharma Parichit2019-11-29T09:21:04Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/953252019-11-29T09:21:04Z2019-11-29T09:21:04ZDCEM was upgraded to version 2.0.0<a href="/packages/DCEM">DCEM</a> was <span class="action">upgraded</span> to version <a href="/packages/DCEM/versions/90589">2.0.0</a><br /><h3>Package description:</h3><p>Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering finite gaussian mixture models for both multivariate and univariate datasets. The initialization is done by randomly selecting the samples from the dataset as the mean (meu) of the Gaussian(s). This version implements the faster alternative EM* that avoids revisiting data by leveraging the heap structure. The algorithm returns a set of Gaussian parameters-posterior probabilities, mean (meu), co-variance matrices (multivariate)/standard-deviation (univariate) and priors. Reference: Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>. This work is partially supported by NCI Grant 1R01CA213466-01.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/909422019-07-24T04:20:56Z2019-07-24T04:20:56ZDCEM was upgraded to version 1.0.0<a href="/packages/DCEM">DCEM</a> was <span class="action">upgraded</span> to version <a href="/packages/DCEM/versions/86455">1.0.0</a><br /><h3>Package description:</h3><p>Implements the Expectation Maximisation (EM)/(EM*) algorithm for clustering finite gaussian mixture models for both multivariate and univariate datasets. The initialization is done by randomly selecting the samples from the dataset as the mean of the Gaussian(s). This version implements the faster alternative EM* that avoids revisiting data by leveraging the heap structure. The algorithm returns a set of Gaussian parameters-posterior probabilities, mean, co-variance matrices (multivariate data)/standard-deviation (for univariate datasets) and priors. Reference: Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>. This work is partially supported by NCI Grant 1R01CA213466-01.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/869322019-04-05T23:20:52Z2019-04-05T23:20:52ZDCEM was upgraded to version 0.0.2<a href="/packages/DCEM">DCEM</a> was <span class="action">upgraded</span> to version <a href="/packages/DCEM/versions/82639">0.0.2</a><br /><h3>Package description:</h3><p>Implements the Expectation Maximisation (EM) algorithm for clustering finite gaussian mixture models for both multivariate and univariate datasets. The initialization is done by randomly selecting the samples from the dataset as the mean of the Gaussian(s). This version improves the parameter initialization on big datasets. The algorithm returns a set of Gaussian parameters-posterior probabilities, mean, co-variance matrices (multivariate data)/standard-deviation (for univariate datasets) and priors. Reference: Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>. This work is partially supported by NCI Grant 1R01CA213466-01.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/800042018-09-30T15:00:42Z2018-09-30T15:00:42ZDCEM was released<a href="/packages/DCEM">DCEM</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering finite gaussian mixture models for both multivariate and univariate datasets. The initialization is done by randomly selecting the samples from the dataset as the mean (meu) of the Gaussian(s). This version implements the faster alternative EM* that avoids revisiting data by leveraging the heap structure. The algorithm returns a set of Gaussian parameters-posterior probabilities, mean (meu), co-variance matrices (multivariate)/standard-deviation (univariate) and priors. Reference: Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>. This work is partially supported by NCI Grant 1R01CA213466-01.</p>crantastic.org