{"title":"一种新的五步数据挖掘算法","authors":"Wang Yiwen","doi":"10.14257/IJDTA.2017.10.1.11","DOIUrl":null,"url":null,"abstract":"Based on the traditional data mining algorithm, a novel data mining algorithm is proposed. This algorithm consists of 5 steps: the first step, set the tree set; the second step, set the window third, subtree contribution; decision tree construction; the fourth step test, positive and negative examples set; the fifth step, expand the achievements window. The experimental study on open source data sets. The results showed that the five step proposed data mining method, not only can build a more concise decision tree, data mining and the accuracy is also higher than the traditional decision tree method.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"26 1","pages":"119-126"},"PeriodicalIF":0.0000,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Novel Five-Step Data Mining Algorithm\",\"authors\":\"Wang Yiwen\",\"doi\":\"10.14257/IJDTA.2017.10.1.11\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the traditional data mining algorithm, a novel data mining algorithm is proposed. This algorithm consists of 5 steps: the first step, set the tree set; the second step, set the window third, subtree contribution; decision tree construction; the fourth step test, positive and negative examples set; the fifth step, expand the achievements window. The experimental study on open source data sets. The results showed that the five step proposed data mining method, not only can build a more concise decision tree, data mining and the accuracy is also higher than the traditional decision tree method.\",\"PeriodicalId\":13926,\"journal\":{\"name\":\"International journal of database theory and application\",\"volume\":\"26 1\",\"pages\":\"119-126\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-01-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of database theory and application\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14257/IJDTA.2017.10.1.11\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of database theory and application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14257/IJDTA.2017.10.1.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Based on the traditional data mining algorithm, a novel data mining algorithm is proposed. This algorithm consists of 5 steps: the first step, set the tree set; the second step, set the window third, subtree contribution; decision tree construction; the fourth step test, positive and negative examples set; the fifth step, expand the achievements window. The experimental study on open source data sets. The results showed that the five step proposed data mining method, not only can build a more concise decision tree, data mining and the accuracy is also higher than the traditional decision tree method.