{"title":"大分类数据互信息计算及其在特征分组中的应用","authors":"Junli Li, Chaowei Zhang, Jifu Zhang, X. Qin","doi":"10.1109/ICDE48307.2020.00210","DOIUrl":null,"url":null,"abstract":"This paper develops a parallel computing system - MiCS - for mutual information of big categorical data on the Spark computing platform. The MiCS algorithm is conductive to processing a large amount and strong repeatability of mutual-information calculation among feature pairs by applying a column-wise transformation scheme. And to improve the efficiency of the MiCS and the utilization rate of Spark cluster resources, we adopt a virtual partitioning scheme to achieve balanced load while mitigating the data skewness problem in the Spark Shuffle process.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"1946-1949"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Computing Mutual Information of Big Categorical Data and Its Application to Feature Grouping\",\"authors\":\"Junli Li, Chaowei Zhang, Jifu Zhang, X. Qin\",\"doi\":\"10.1109/ICDE48307.2020.00210\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper develops a parallel computing system - MiCS - for mutual information of big categorical data on the Spark computing platform. The MiCS algorithm is conductive to processing a large amount and strong repeatability of mutual-information calculation among feature pairs by applying a column-wise transformation scheme. And to improve the efficiency of the MiCS and the utilization rate of Spark cluster resources, we adopt a virtual partitioning scheme to achieve balanced load while mitigating the data skewness problem in the Spark Shuffle process.\",\"PeriodicalId\":6709,\"journal\":{\"name\":\"2020 IEEE 36th International Conference on Data Engineering (ICDE)\",\"volume\":\"1 1\",\"pages\":\"1946-1949\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 36th International Conference on Data Engineering (ICDE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE48307.2020.00210\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE48307.2020.00210","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computing Mutual Information of Big Categorical Data and Its Application to Feature Grouping
This paper develops a parallel computing system - MiCS - for mutual information of big categorical data on the Spark computing platform. The MiCS algorithm is conductive to processing a large amount and strong repeatability of mutual-information calculation among feature pairs by applying a column-wise transformation scheme. And to improve the efficiency of the MiCS and the utilization rate of Spark cluster resources, we adopt a virtual partitioning scheme to achieve balanced load while mitigating the data skewness problem in the Spark Shuffle process.
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