An Efficient MapReduce-Based Parallel Clustering Algorithm for Distributed Traffic Subarea Division

Algorithm 1

Map(key, value).

Input:

  key: the offset,

  value: the sample,

  centroids: the global variable.

Output: ,

   : the index of the closest centroid,

   : the information of sample.

(1) Construct a global variable centroids including the information of the closet centroid point;

(2) Construct the sample examples to extract the data objects from value;

(3) min_Dis = Double.MAX_VALUE;

(4) index = −1;

(5)  for   to centroids.length  do

(6)    distance = DistanceFunction(examples, centroids[]);

(7)    if distance < min_Dis  then

(8)    min_Dis = distance;

(9)    index = ;

(10)  end if

(11)  end for

(12) index = ;

(13) Construct value ₁ as a string consisting of the values of different dimensions;

(14) return   pairs;