%k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：%同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。%k-means 算法的工作过程说明如下：首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，%则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；%然后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；%不断重复这一过程直到标准测度函数开始收敛为止。一般都采用均方差作
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SELECT `a`.`aid`,`a`.`title`,`a`.`create_time`,`m`.`username` FROM `jrk_downrecords` `a` INNER JOIN `jrk_member` `m` ON `a`.`uid`=`m`.`id` WHERE `a`.`status` = 1 GROUP BY `a`.`aid` ORDER BY `a`.`create_time` DESC LIMIT 10 [ RunTime:0.090822s ]
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SHOW FULL COLUMNS FROM `jrk_member` [ RunTime:0.002265s ]
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SHOW FULL COLUMNS FROM `jrk_searchrecords` [ RunTime:0.002091s ]
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SHOW FULL COLUMNS FROM `jrk_articles` [ RunTime:0.002298s ]
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