Shulou(Shulou.com)06/01 Report--

In order to solve this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

The first is map.

Public static class KMmap extends Mapper {/ / Central set / / the cluster set here is the self-set centersPath, that is, the path where the collection is stored in hdfs ArrayList centers = null; / / with k centers int k = 0 / / read protected void setup (Context context) throws IOException, InterruptedException {/ / getCentersFromHDFS method is to pass a Path to get an ArrayList collection centers = Utils.getCentersFromHDFS (context.getConfiguration (). Get ("centersPath"), false); k = centers.size ();} / * 1. Read one entry to be classified at a time and compare it with the center and classify it to the corresponding center * 2. The center ID is the key, and the record contained in the center is the value output (for example: 10.2). 1 the ID,0.2 that is the cluster center is a value near the cluster center) * / @ Override protected void map (LongWritable key, Text value,Context context) throws IOException, InterruptedException {ArrayList fileds = Utils.textToArray (value); / / the textToArray method converts the row value entered by map into the set int sizeOfFileds = fileds.size () of ArrayList after segmentation. Double minDistance = 99999999; int centerIndex = 0; / / take out the k center points and the currently read records in turn to calculate for (int iS0uti

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