Outliers are infrequent observations, that is, the data points that do not appear to follow the characteristic distribution of the rest of the data. They appear far away and diverge from the overall pattern of the data. These might occur due to measurement errors or other anomalies which result in wrong estimations. Outliers can be univariate and multivariate. Univariate outliers can be determined by looking at the distribution of a single variable whereas multivariate outliers are present in an n-dimensional space which can be found by looking at the distributions in multi-dimensions.
To step through this recipe, you will need a running Spark cluster in any one of the modes, that is, local, standalone, YARN, or Mesos. For installing Spark on a standalone cluster, please refer http://spark.apache.org/docs/latest/spark-standalone.html. Also, include the Spark MLlib package in the build.sbt file so that it downloads the related libraries and the API can be used...
