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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) 2
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Machine Learning Types • Supervised learning – Classification – Regression/Prediction • Unsupervised learning – Clustering • Semi-supervised learning • Association Analysis • Reinforcement learning
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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. 4
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Data Mining/KDD Definition := “KDD is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” (Fayyad) Applications: • Retail: Market basket analysis, Customer relationship management (CRM) • Finance: Credit scoring, fraud detection • Manufacturing: Optimization, troubleshooting • Medicine: Medical diagnosis • Telecommunications: Quality of service optimization • Bioinformatics: Motifs, alignment • ... 5
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Machine Learning & Data Mining • Machine learning focuses on prediction, based on known properties learned from the training data. • Data mining focuses on the discovery of (previously) unknown properties in the data. This is the analysis step of Knowledge Discovery in Databases. • Data mining uses many machine learning methods, but often with a slightly different goal in mind • Machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. 6
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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 Inter-cluster Intra-cluster distances are distances are maximized minimized
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Applications of Cluster Analysis • Understanding Discovered Clusters Industry Group 1 AppliedCabletron-Matl--DOWN,BaySys-DOWN,CISCO-Network-DOWN,HP-Down,3-COM-DOWN,-DOWN, DSC-Comm-DOWN,INTEL-DOWN,LSI-Logic-DOWN, Technology1-DOWN – Group related documents for MicronNatl-Semiconduct-Tech-DOWN,Texas-DOWN,Oracl-Inst-Down,Tellabs-DOWN,SGI--IncDO-WN,Down, browsing, group genes and Sun-DOWN 2 AppleADV--CompMicro--DOWN,AutodeskDevice-DOWN,Andrew-DOWN,DEC-Corp-DOWN,-DOWN, proteins that have similar Computer-Assoc-DOWN,Circuit-City-DOWN, Technology2-DOWN Compaq-DOWN, EMC-Corp-DOWN, Gen-Inst-DOWN, Motorola-DOWN,Microsoft-DOWN,Scientific-Atl-DOWN functionality, or group stocks 3 MBNAFannie--MaeCorp-DOWN,Fed-DOWN,Morgan-Home-Stanley-Loan--DOWN,DOWN Financial-DOWN with similar price fluctuations 4 BakerLouisiana-Hughes--LandUP,Dresser-UP,Phillips-Inds--UP,HalliburtonPetro-UP,Unocal-HLD-UP,-UP, Oil-UP Schlumberger-UP • Summarization – Reduce the size of large data sets Clustering precipitation in Australia
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Types of Clusterings • A clustering is a set of clusters • Important distinction between hierarchical and partitional sets 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
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Hierarchical Clustering p1 p3 p4 p2 p1 p2 p3 p4 Traditional Hierarchical Clustering Traditional Dendrogram p1 p3 p4 p2 p1 p2 p3 p4 Non-traditional Hierarchical Clustering Non-traditional Dendrogram
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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
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Clustering Algorithms • K-means and its variants • Hierarchical clustering • Density-based clustering
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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
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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.
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