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Hadoop大数据解决方案进阶应用Mahout数据挖掘工具 (6)Hadoop讲师:迪伦(北风网版权所有)课程目标 K-means聚类实例 Canopy算法 Mahout分类算法K-means聚类实例步骤4:将数据转化为SequenceFile格式$ hadoop fs -mkdir reuters-out$ hadoop fs -put reuters-out reuters-outK-means聚类实例步骤5:数据向量化步骤6:执行K-means聚类步骤7:导出结果Canopy算法-寻找最优的初始点Canopy算法使用“-cl”参数,启用canopy算法舍弃“-k”参数,否则canopy选取的点将被随机点覆盖Mahout分类关键词Key idea Description Model A computer program that makes decisions; in classification, the output of the training algorithm is a model Training Data Subset of training examples labeled with the value of the target variable and used as input to the learning algorithm to produce the model Test Data Withheld portion of training examples given to the model without the value for the target variable (although the value is known) and used to evaluate the model Training Learning process that uses training data to produce a model. That model can then compute estimates of the target variable given the predictor variables as inputs. Training example Entity with features that will be used as input for learning algorithm Feature A known characteristic of a training or new example; a “feature” is equivalent to saying a “characteristic”. Variable In this context, a variable is equivalent to a the value of a feature or a function of several features. This usage is somewhat different from a normal variable in a computer program. Record A container where an example is stored; such a record is composed of fields. Field Part of a record that contains the value of a feature (variable) Predictor variable Feature selected for use as input for a classification model. Not all features need be used. Some features may be algorithmic combinations of other features. Target variable Feature that the classification model is attempting to estimate: the target variable is categorical and its determination is the aim of the classification system. Mahout分类中的数据类型Type of Value Description Continuous This type of value is a floating point value. This might be a price, a weight, a time, a
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