Machine learning hands on clustering
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This document discusses machine learning clustering techniques. It begins by explaining the difference between supervised and unsupervised learning, with clustering falling under unsupervised learning. The document then discusses the k-means clustering algorithm in detail, including how it works, its strengths and weaknesses, and the steps involved. It also covers considerations for k-means performance and provides examples of frameworks and platforms for machine learning.
Machine learning hands on clustering
- 2. WHAT’S IN THE MENU - RECOMMENDATIONS 1. Why so popular 2. Supervised vs Unsupervised Learning 3. Topic2 4. Topic3 5. Topic4 6. Wrap-up
- 4. WHY IS MACHINE LEARNING (CS 229) THE MOST POPULAR COURSE AT STANFORD? - ANDREW NG
- 5. WHAT CAN YOU TELL ME ABOUT X? Supervised vs unsupervised learning Typical methods: regression and classification Given an object with observed set of features X1, …., Xn having an response Y, the goal is to predict Y using X1, …., Xn Typical methods: principal component analysis (PCA), expectation maximization (EM) and clustering (k-means and its variations) Given an object with observed set of features X1, …., Xn, the goal is to discover relationships or groups between variables or observations. Clustering algorithms try to find natural grouping in data and therefore similar datasets.
- 6. APPLICATIONS Market segmentation : given market research results, how you can find the best customer segments Anomaly detection : find fraud, detect network attacks, or discover problems in servers or other sensor-equipped machinery. Is important to be able to find new types of anomalies that have never seen before. Healthcare: accident prone factor of the area to hospital assignment, gene clustering
- 7. GROUPING UNLABELED ITEMS USING K-MEANS CLUSTERING SWAT Strengths : Will always converge Scales well Weakness : Can converge at local minima Slow on very large datasets Choosing the wrong k Advantages : Easy to implement
- 8. GROUPING UNLABELED ITEMS USING K-MEANS CLUSTERING
- 9. SIMILARITY There are several ways on measuring similarity between observations. Manhattan distance Euclidian distance Cosine distance
- 10. K-MEANS PSEUDO CODE Randomly create k points for starting centroids ---------------------------------------------------------------- For every point assigned to a centroid Calculate the distance between the centroid and point Assign the point to the cluster with the lowest distance ---------------------------------------------------------------- For every cluster calculate the mean of the points in that cluster Assign the centroid to the mean While any point has changed cluster assignment Repeat until convergence Cluster assignment step Move centroid step
- 11. COST FUNCTION & RANDOM INITIALIZATION for i = 1 to 100 { randomly initialize k-means run k-means and get centroids positions c(1 to m) and µ(1 to K) compute cost function J(c(1 to m), µ(1 to K)) } Pick clustering that gave lowest J(c(1 to m), µ(1 to K)) Cluster assignment step: minimize J c(1 to m) while holding µ(1 to K) fixed Move centroid step: minimize J with respect to µ(1 to K)
- 12. PERFORMANCE CONSIDERATION K-means The K-means has the computational complexity of O(iKnm), i is the number of iterations, K the number of clusters, n the number of observations, m the number of features. Improvements: •Reducing the average number of iterations. •Parallel implementation of K-means by leveraging Hadoop or Spark. •Reducing the number of outliers and possible features by noise filtering with a smoothing algorithm. •Decreasing the dimensions of the model.
- 13. FRAMEWORKS Java : Weka, Mahout, spark Python: scikit-learn, py-spark, Pylearn2 (Theano) C ++: Shogun .NET: Encog https://github.com/josephmisiti/awesome-machine-learning
- 14. PLATFORMS - IBM BLUEMIX
- 15. PLATFORMS – MICROSOFT AZURE ML
- 17. BOOKS