CS699 Lecture 5 Classification 2 • Using IF‐THEN Rules for Classification Represent the knowledge in the form of IF‐THEN rules • Assessment of a rule: coverage and accuracy – A tuple is covered by R if it satisfies the antecedent of R – ncovers = # of tuples covered by R – ncorrect = # […]
CS699 Lecture 1 Introduction • Our focus is “data mining” not “data warehousing.” • Will discuss – Data preprocessing CS699 • Data mining is an important component of data analysis. – Basic data mining algorithms – How to evaluate data mining models and data mining results – How to perform data mining using software tools
CS699 Lecture 10 Clustering What is Cluster Analysis? Cluster: A collection of data objects similar (or related) to one another within the same group dissimilar (or unrelated) to the objects in other groups Cluster analysis (or clustering, data segmentation, …) Finding similarities between data according to the characteristics found in
CS699 Lecture 2 Data Exploration Types of Data Sets – Data matrix, e.g., numerical matrix, crosstabs • Record – Relational records – Document data: text documents: term‐ frequency vector – Transaction data • Graph and network – World Wide Web – Social or information networks – Molecular Structures • Ordered – Video data: sequence of
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CS699 Lecture 6 Performance Evaluation Model Evaluation and Selection Evaluation metrics: How can we measure accuracy? Other metrics to consider? Use an independent test dataset instead of training dataset when assessing accuracy Methods for estimating a classifier’s accuracy: Holdout method, random subsampling Cross‐validation Bootstrap Comparing classifiers: Confidence
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CS699 Lecture 8 Association Rule Mining What Is Frequent Pattern Analysis? Frequent pattern: a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set First proposed by Agrawal, Imielinski, and Swami [AIS93] in the context of frequent itemsets and association rule mining Motivation:Findinginherentregularitiesindata What products were
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CS699 Lecture 9 Correlation Analysis Other Frequent Pattern Mining Association Rule Mining on Weka Data preparation When performing association rule mining on a transactional data using Weka, the dataset must be converted to an appropriate form. Each item becomes an attribute. Each attribute takes on only single value, e.g., {1} or
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INF 553: Foundations and Applications of Data Mining (Summer 2020) Yao-Yi Chiang Associate Professor (Research), Spatial Sciences Institute Associate Director, Integrated Media Systems Center Spatial Computing Lab University of Southern California Thanks for source slides and material to: J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets http://www.mmds.org What is Data Mining? About THIS
Finding Frequent Itemsets: Limited Pass Algorithms Thanks for source slides and material to: J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets http://www.mmds.org 1 Limited Pass Algorithms ! Algorithms so far: compute exact collection of frequent itemsets of size k in k passes ! A-Priori, PCY, Multistage, Multihash ! Many applications where it is
Mining Data Streams INF 553 Slides from: Wensheng Wu and Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Stanford University http://www.mmds.org 1 Roadmap • Motivation • Sampling – Fixed-portion & fixed-size (reservoir sampling) • Filtering – Bloom filter • Counting – Estimating # of distinct values, moments • Sliding window – Counting #
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