Machine learning clustering

Machine learning clustering

• 1.
  Mauritius JEDI Machine Learning & BigData Clustering Algorithms Nadeem Oozeer
• 2.
  Machine learning: • Supervisedvs Unsupervised. – Supervised learning - the presence of the outcome variable is available to guide the learning process. • there must be a training data set in which the solution is already known. – Unsupervised learning - the outcomes are unknown. • cluster the data to reveal meaningful partitions and hierarchies
• 3.
  Clustering: • Clustering isthe task of gathering samples into groups of similar samples according to some predefined similarity or dissimilarity measure sample Cluster/group
• 4.
  • In thiscase clustering is carried out using the Euclidean distance as a measure.
• 5.
  Clustering: • What isclustering good for – Market segmentation - group customers into different market segments – Social network analysis - Facebook "smartlists" – Organizing computer clusters and data centers for network layout and location – Astronomical data analysis - Understanding galaxy formation
• 6.
  Galaxy Clustering: • Multi-wavelengthdata obtained for galaxy clusters – Aim: determine robust criteria for the inclusion of a galaxy into a cluster galaxy – Note: physical parameters of the galaxy cluster can be heavily influenced by wrong candidate Credit: HST
• 7.
  Clustering Algorithms : •Hierarchy methods – statistical method used to build a cluster by arranging elements at various levels
• 8.
  Dendogram: • Each levelwill then represent a possible cluster. • The height of the dendrogram shows the level of similarity that any two clusters are joined • The closer to the bottom they are the more similar the clusters are • Finding of groups from a dendrogram is not simple and is very often subjective
• 9.
  • Partitioning methods –make an initial division of the database and then use an iterative strategy to further divide it into sections – here each object belongs to exactly one cluster Credit: Legodi, 2014
• 10.
  K-means:
• 11.
  K-means algorithm: 1. Givenn objects, initialize k cluster centers 2. Assign each object to its closest cluster centre 3. Update the center for each cluster 4. Repeat 2 and 3 until no change in each cluster center • Experiment: Pack of cards, dominoes • Apply the K-means algorithm to the Shapley data – Change the number of potential cluster and find how the clustering differ
• 12.
  K Nearest Neighbors(k-NN): • One of the simplest of all machine learning classifiers • Differs from other machine learning techniques, in that it doesn't produce a model. • It does however require a distance measure and the selection of K. • First the K nearest training data points to the new observation are investigated. • These K points determine the class of the new observation.
• 13.
  1-NN • Simple idea:label a new point the same as the closest known point Label it red.
• 14.
  1-NN Aspects ofan Instance-Based Learner 1. A distance metric – Euclidian 2. How many nearby neighbors to look at? – One 3. A weighting function (optional) – Unused 4. How to fit with the local points? – Just predict the same output as the nearest neighbor.
• 15.
  k-NN • Generalizes 1-NNto smooth away noise in the labels • A new point is now assigned the most frequent label of its k nearest neighbors Label it red, when k = 3 Label it blue, when k = 7