{"id":7535,"date":"2025-10-13T07:02:31","date_gmt":"2025-10-13T07:02:31","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/10\/13\/clustring_very_different_values\/"},"modified":"2025-10-13T07:02:31","modified_gmt":"2025-10-13T07:02:31","slug":"clustring_very_different_values","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/10\/13\/clustring_very_different_values\/","title":{"rendered":"Clustring very different values"},"content":{"rendered":"<p>    Clustring very different values<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p>I have 200 observations, 3 variables ( somewhat correlated).For v1, the median is 300 dollars. but I have a really long tail. when I do the histogram, 100 obs are near 0 and the others form a really long tail, even when I cap outliers. what is best way to cluster?<\/p>\n<\/p><\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/Due-Duty961\"> \/u\/Due-Duty961 <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1o37n2r\/clustring_very_different_values\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1o37n2r\/clustring_very_different_values\/\">[comments]<\/a><\/span>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    \/u\/Due-Duty961<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1o37n2r\/clustring_very_different_values\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Clustring very different values I have 200 observations, 3 variables ( somewhat correlated).For v1, the median is 300 dollars. but I have a really long tail. when I do the histogram, 100 obs are near 0 and the others form a really long tail, even when I cap outliers. what is best way to cluster? [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,99],"tags":[3991,3993,3992],"class_list":["post-7535","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-datascience","tag-clustring","tag-different","tag-very"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/7535"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=7535"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/7535\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=7535"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=7535"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=7535"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}