Code example of K-Means algorithm implemented by Spark
K-Means algorithm is a distance-based clustering algorithm, which uses iterative method to calculate K clustering centers and cluster several points into K-class.
MLlib implements the K-Means algorithm by running multiple K-Means algorithms, each called run, to return the cluster center of the best cluster. The initial cluster center can be random or KMean | | obtained, when the iteration reaches a certain number of times, or when all run converges, the algorithm ends.
To implement the K-Means algorithm with Spark, first modify the pom file and introduce the machine learning MLlib package:
Org.apache.spark spark-mllib_2.10 1.6.0
Code:
Import org.apache.log4j. {Level,Logger} import org.apache.spark. {SparkContext SparkConf} import org.apache.spark.mllib.clustering.KMeansimport org.apache.spark.mllib.linalg.Vectorsobject Kmeans {def main (args: array [string]) = {/ / block log Logger.getLogger ("org.apache.spark"). SetLevel (Level.WARN) Logger.getLogger ("org.apache.jetty.server") .setLevel (Level.OFF) / / set the runtime environment val conf = new SparkConf (). SetAppName ("K-Means"). SetMaster ("spark:/") / master:7077 ") .setJars (Seq (" E:\ Intellij\\ Projects\\ SimpleGraphX\\ SimpleGraphX.jar ") val sc = new SparkContext (conf) / / load dataset val data = sc.textFile (" hdfs://master:9000/kmeans_data.txt ") 1) val parsedData = data.map (s = > Vectors.dense (s.split ("). Map (_ .toDouble)) / / clustering the data into two classes and 20 iterations to form the data model val numClusters = 2 val numIterations = 20 val model = KMeans.train (parsedData, numClusters) NumIterations) / / the central point of the data model println ("Cluster centres:") for (c val linevectore = Vectors.dense (line.split ("). Map (_ .toDouble)) val prediction = model.predict (linevectore) line +" + prediction} .foreach (println) sc.stop}
Use the textFile () method to load the dataset, get the RDD, and then use the KMeans.train () method to get a KMeans model based on the RDD, K value, and iterations. Once you have the KMeans model, you can determine which class a set of data belongs to. The specific method is to generate a Vector with the Vectors.dense () method, and then use the KMeans.predict () method to return which class it belongs to.
Running result:
Cluster centres: [Within Set Sum of Squared Errors = 943.2074999999998Vectors 7.31.5 10.9 is belong to cluster:0Vectors 4.2 11.2 2.7 is belong to cluster:0Vectors 18.04.5 3.8 is belong to cluster:10.0 0.0 5.00.1 10.1 0.1 01.2 5.2 13.5 09.5 9.0 9.09.1 9.19 . 1 019.2 9.4 29.2 05.8 3.0 18.0 03.5 12.2 60.0 13.6 7.9 8.1 0
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