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Machine Learning & Data Mining

Machine Learning & Data Mining. What is Machine Learning?. a branch of artificial intelligence, concerns the construction and study of systems that can learn from data .

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Machine Learning & Data Mining

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  1. Machine Learning & Data Mining

  2. What is Machine Learning? • a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. • The core of machine learning deals with representation and generalization: Representation of data instances and functions evaluated on these instances are part of all machine learning systems. Generalization is the property that the system will perform well on unseen data instances • Tom M. Mitchell: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E” From Wikipedia (Machine Learning)

  3. Machine Learning Types • Supervised learning • Classification • Regression/Prediction • Unsupervised learning • Clustering • Semi-supervised learning • Association Analysis • Reinforcement learning

  4. Growth of Machine Learning • Machine learning is preferred approach to • Speech recognition, Natural language processing • Computer vision • Medical outcomes analysis • Robot control • Computational biology • This trend is accelerating • Improved machine learning algorithms • Improved data capture, networking, faster computers • Software too complex to write by hand • New sensors / IO devices • Demand for self-customization to user, environment • It turns out to be difficult to extract knowledge from human expertsfailure of expert systems in the 1980’s.

  5. Data Mining/KDD Definition := “KDD is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” (Fayyad) • Retail: Market basket analysis, Customerrelationshipmanagement (CRM) • Finance: Creditscoring, frauddetection • Manufacturing: Optimization, troubleshooting • Medicine: Medicaldiagnosis • Telecommunications: Quality of service optimization • Bioinformatics: Motifs, alignment • ... Applications:

  6. Machine Learning & Data Mining • Machine learningfocuses on prediction, based on knownpropertieslearnedfromthetraining data. • Data miningfocuses on thediscovery of (previously) unknownproperties in the data. This is theanalysis step of Knowledge Discovery in Databases. • Data miningusesmanymachinelearningmethods, but oftenwith a slightlydifferentgoal in mind • Machine learningalsoemploys data miningmethods as "unsupervisedlearning" or as a preprocessing step toimprovelearneraccuracy.

  7. Statistics Pattern Recognition Machine Learning Data Mining AI Big Data Database systems

  8. Unsupervised Learning: Cluster Analysis

  9. Inter-cluster distances are maximized Intra-cluster distances are minimized What is Cluster Analysis? • Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups

  10. Applications of Cluster Analysis • Understanding • Group related documents for browsing, group genes and proteins that have similar functionality, or group stocks with similar price fluctuations • Summarization • Reduce the size of large data sets Clustering precipitation in Australia

  11. How many clusters? Six Clusters Two Clusters Four Clusters Notion of a Cluster can be Ambiguous

  12. Types of Clusterings • A clustering is a set of clusters • Important distinction between hierarchical and partitionalsets of clusters • Partitional Clustering • A division data objects into non-overlapping subsets (clusters) such that each data object is in exactly one subset • Hierarchical clustering • A set of nested clusters organized as a hierarchical tree

  13. A Partitional Clustering Partitional Clustering Original Points

  14. Hierarchical Clustering Traditional Hierarchical Clustering Traditional Dendrogram Non-traditional Hierarchical Clustering Non-traditional Dendrogram

  15. Other Distinctions Between Sets of Clusters • Exclusive versus non-exclusive • In non-exclusive clusterings, points may belong to multiple clusters. • Can represent multiple classes or ‘border’ points • Fuzzy versus non-fuzzy • In fuzzy clustering, a point belongs to every cluster with some weight between 0 and 1 • Weights must sum to 1 • Probabilistic clustering has similar characteristics • Partial versus complete • In some cases, we only want to cluster some of the data • Heterogeneous versus homogeneous • Cluster of widely different sizes, shapes, and densities

  16. Clustering Algorithms • K-means and its variants • Hierarchical clustering • Density-based clustering

  17. K-means Clustering • Partitional clustering approach • Each cluster is associated with a centroid (center point) • Each point is assigned to the cluster with the closest centroid • Number of clusters, K, must be specified • The basic algorithm is very simple

  18. K-means Clustering – Details • Initial centroids are often chosen randomly. • Clusters produced vary from one run to another. • The centroid is (typically) the mean of the points in the cluster. • ‘Closeness’ is measured by Euclidean distance, cosine similarity, correlation, etc.

  19. K-means Clustering – Details • K-means will converge for common similarity measures mentioned above. • Most of the convergence happens in the first few iterations. • Often the stopping condition is changed to ‘Until relatively few points change clusters’ • Complexity is O( n * K * I * d ) • n = number of points, K = number of clusters, I = number of iterations, d = number of attributes

  20. Most common measure is Sum of Squared Error (SSE) For each point, the error is the distance to the nearest cluster To get SSE, we square these errors and sum them. x is a data point in cluster Ci and mi is the representative point for cluster Ci can show that micorresponds to the center (mean) of the cluster Given two clusters, we can choose the one with the smallest error One easy way to reduce SSE is to increase K, the number of clusters A good clustering with smaller K can have a lower SSE than a poor clustering with higher K Evaluating K-means Clusters

  21. Issues and Limitations for K-means • How to choose initial centers? • How to choose K? • How to handle Outliers? • Clusters different in • Shape • Density • Size

  22. Optimal Clustering Sub-optimal Clustering Two different K-means Clusterings Original Points

  23. Importance of Choosing Initial Centroids

  24. Importance of Choosing Initial Centroids

  25. Importance of Choosing Initial Centroids …

  26. Importance of Choosing Initial Centroids …

  27. Problems with Selecting Initial Points • If there are K ‘real’ clusters then the chance of selecting one centroid from each cluster is small. • Chance is relatively small when K is large • If clusters are the same size, n, then • For example, if K = 10, then probability = 10!/1010 = 0.00036 • Sometimes the initial centroids will readjust themselves in ‘right’ way, and sometimes they don’t • Consider an example of five pairs of clusters

  28. Solutions to Initial Centroids Problem • Multiple runs • Helps, but probability is not on your side • Sample and use hierarchical clustering to determine initial centroids • Select more than k initial centroids and then select among these initial centroids • Select most widely separated • Postprocessing • Bisecting K-means • Not as susceptible to initialization issues

  29. Hierarchical Clustering • Produces a set of nested clusters organized as a hierarchical tree • Can be visualized as a dendrogram • A tree like diagram that records the sequences of merges or splits

  30. Strengths of Hierarchical Clustering • Do not have to assume any particular number of clusters • Any desired number of clusters can be obtained by ‘cutting’ the dendogram at the proper level • They may correspond to meaningful taxonomies • Example in biological sciences (e.g., animal kingdom, phylogeny reconstruction, …)

  31. Hierarchical Clustering • Two main types of hierarchical clustering • Agglomerative: • Start with the points as individual clusters • At each step, merge the closest pair of clusters until only one cluster (or k clusters) left • Divisive: • Start with one, all-inclusive cluster • At each step, split a cluster until each cluster contains a point (or there are k clusters) • Traditional hierarchical algorithms use a similarity or distance matrix • Merge or split one cluster at a time

  32. Agglomerative Clustering Algorithm • More popular hierarchical clustering technique • Basic algorithm is straightforward • Compute the proximity matrix • Let each data point be a cluster • Repeat • Merge the two closest clusters • Update the proximity matrix • Until only a single cluster remains • Key operation is the computation of the proximity of two clusters • Different approaches to defining the distance between clusters distinguish the different algorithms

  33. Start with clusters of individual points and a proximity matrix p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . Starting Situation Proximity Matrix

  34. After some merging steps, we have some clusters C1 C2 C3 C4 C5 C1 C2 C3 C4 C5 Intermediate Situation C3 C4 C1 Proximity Matrix C5 C2

  35. We want to merge the two closest clusters (C2 and C5) and update the proximity matrix. C1 C2 C3 C4 C5 C1 C2 C3 C4 C5 Intermediate Situation C3 C4 C1 Proximity Matrix C5 C2

  36. The question is “How do we update the proximity matrix?” After Merging C2 U C5 C1 C3 C4 C1 ? C3 ? ? ? ? C2 U C5 C4 C3 ? ? C4 C1 Proximity Matrix C2 U C5

  37. p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . How to Define Inter-Cluster Similarity Similarity? • MIN • MAX • Group Average • Distance Between Centroids • Other methods driven by an objective function • Ward’s Method uses squared error Proximity Matrix

  38. p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . How to Define Inter-Cluster Similarity • MIN • MAX • Group Average • Distance Between Centroids • Other methods driven by an objective function • Ward’s Method uses squared error Proximity Matrix

  39. p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . How to Define Inter-Cluster Similarity • MIN • MAX • Group Average • Distance Between Centroids • Other methods driven by an objective function • Ward’s Method uses squared error Proximity Matrix

  40. p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . How to Define Inter-Cluster Similarity • MIN • MAX • Group Average • Distance Between Centroids • Other methods driven by an objective function • Ward’s Method uses squared error Proximity Matrix

  41. p1 p2 p3 p4 p5 . . . p1 p2 p3 p4 p5 . . . How to Define Inter-Cluster Similarity   • MIN • MAX • Group Average • Distance Between Centroids • Other methods driven by an objective function • Ward’s Method uses squared error Proximity Matrix

  42. 1 2 3 4 5 Cluster Similarity: MIN or Single Link • Similarity of two clusters is based on the two most similar (closest) points in the different clusters • Determined by one pair of points, i.e., by one link in the proximity graph.

  43. 5 1 3 5 2 1 2 3 6 4 4 Hierarchical Clustering: MIN Nested Clusters Dendrogram

  44. Two Clusters Strength of MIN Original Points • Can handle non-elliptical shapes

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