1. There are plenty of tools that can be used to implement k- k-means clustering algorithm. 
2.  
3. Java: If we use java, download Weka(Data Mining with Open Source Machine Learning Software in Java) and either use their API in your code or the GUI. the function to use is SimpleKMeans
4.  
5. MATLAB: By using this we can a. Cluster data using k-means clustering, then plot the cluster regions. b. Partition Data into Two clusters. c. Cluster Data using Parallel Computing Clustering large data sets might take time, particularly if you use online updates. If you have Parallel Computing Toolbox then kmeans runs each clustering task(or replicate) in parallel. d. Assign New Data to Existing Clusters and Generate C/C++ code.
6.  
7. Python: k-means++ selects initial cluster centroids using sampling based on an empirical probability distribution of the points. this technique speeds up convergence. There are four steps to implement: a. Data Pre-processing b. Finding the optimal number of clusters using the elbow method c. Training the K-means algorithm on the training dataset d. Visualizing the clusters.
8.  
9. Apache Spark: K-means is one of the most commonly used clustering algorithms that clusters the data points into a predefined number of clusters. The spark.mllib implementation includes a parallelized variant of the k-means++ method called k means.
10.  
11. 
    
12. Pros of implementing K-means:
13. 
    
15. 1. High Performance 2. Easy to use 3. Guarantee convergence 4. Result Interpretation
17. 
    
19. Cons of implementing K-means:
22. Result repeatability
24. Spherical clustering only
26. Manual work
28. Clustering outliers
33.