EECS4412

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Handouts\\
***General\\
{{:activereading.pdf|active reading}}\\
{{:paperwriting.pdf|paper writing}}\\
{{:representational_typology1.pdf|representational typology}}\\
{{:machinelearning.pdf|introduction to machine learning concepts}}\\
{{:heuristics.pdf|heuristics}}\\
{{:logic.pdf|logic}}\\
{{:data_mining_tasks.pdf|data mining tasks}}\\
{{:rough-set-intro.pdf|rough sets introduction}}\\
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*****Early data mining - the DBLEARN family**\\
{{:b14435287.pdf|Yandong Cai's thesis - DBLEARN}}\\
{{:j.1467-8640.1991.tb00387.x.pdf|Learning in relational databases: an attribute-oriented approach}}\\
{{:vldb92.pdf|Knowledge Discovery in Databases: An Attribute-Oriented Approach}}\\
{{:9404.pdf|The Software Architecture of DBLEARN}}\\
{{:9405.pdf|Performance Improvement in the Implementation of DBLEARN}}\\
{{:b14264018.pdf|Tony Hu's MSc thesis - Conceptual Clustering}}\\
{{:huphd.pdf|Tony Hu's PhD thesis - DBROUGH}}\\
{{:j.1467-8640.1995.tb00035.x.pdf|Learning in Relational Databases: a Rough Set Approach}}\\
{{:9902.pdf|A Tutorial Guide to DBDISCOVER}}\\
{{:00632290.pdf|Data Visualization in the DB-Discover System}}\\
{{:9606.pdf|RIAC: A Rule Induction Algorithm Based on Approximate Classification}}\\
{{:9506.pdf|Performance Evaluation of Attribute-Oriented Algorithms for Knowledge Discovery from Databases: Extended Report}}\\
{{:dbrough.pdf|DBROUGH: A Rough Set Based Knowledge Discovery System}}\\
{{:00492093.pdf|Mining Knowledge Rules flom Databases: A Rough Set Approach}}\\
{{:rough_sets_similarity_based_learning_from_databases.pdf|Rough Sets Similarity-Based Learning from Databases}}\\
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*****Some stuff on rough sets**\\
{{:rough-set-intro.pdf|Very Gentle Introduction to Rough Sets}}\\
{{:dealing_with_noisy_data_in_the_rough_set_method.pdf|Noisy Data in Rough Sets}}\\
{{:komorowski98_rough_sets_tutorial.pdf|Rough Sets: A Tutorial - Komorowski et al.}}\\
{{:pawlak_skowron_bull_irss.pdf|Rough Set Rudiments - Pawlak et a.}}\\
{{:rough_sets_theory.pdf|Tutorial Rough sets theory - Walczak et al.}}\\
{{:zpawlak.pdf|Rough Sets -Pawlak}}\\
{{:zsuraj_tutorial_on_rough_stes.pdf|An Introduction to Rough Set Theory and Its Applications: A tutorial - Suraj}}\\
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*****Some stuff on ELEM2**\\
{{:papercai-98-fn.pdf|ELEM2: A Learning System for More Accurate Classifications}}\\
{{:0046352955212ded29000000.pdf|Discretization of Continuous Attributes for Learning Classication Rules}}\\
{{:ci01.pdf|Rule Quality Measures Improve the Accuracy of Rule Induction}}\\
{{:elem2_user_manual_v3.pdf|ELEM2 Rule Induction System Manual}}\\
{{:tkde99.pdf|Rule-Induction and Case-Based Reasoning}}\\
{{:01046978.pdf|From Computational Intelligence to Web Intelligence}}\\
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*****What every computer scientist should know**\\
{{:basic_neural_networks.pdf|A Basic Introduction to Neural Networks}}\\
{{:basic_ngenetic_algorithm.pdf|Tutorial- Basic Genetic Algorithm in plain English}}\\
{{:bp_neural_networks.pdf|When to use (not use) neural networks}}\\
{{:greedy_algorithm.pdf|Greedy algorithm}}\\
{{:simulated_annealing.pdf|Simulated annealing}}\\
{{:hill_climbinbg.pdf|Hill climbing}}\\
{{:tabu_search.pdf|Tabu search}}\\
{{:beam_search.pdf|Beam search}}\\
{{:np.pdf|np}}\\
{{:decision_trees.pdf|decision trees}}\\
{{:gradient_descent_-_wikipedia_the_free_encyclopedia.pdf|gradient decent}}\\
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*******EM Algorithm****\\
{{:em.pdf|Tutorial on the EM Algorithm and its Application}}\\
{{:what_is_the_em.pdf|What is the EM Algorithm}}\\
{{:em-talk.pdf|EM Talk}}\\
{{:emalgorithm.pdf|EM Algorithm slides}}\\
{{:gregory_nuel_lecture2.pdf|Expectation-Maximization and Hidden Markov Models}}\\
{{:1-s2.0-s089812210800103x-main.pdf|Applying the extended mass-constraint EM algorithm to image retrieval}}\\
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*******Recommender Systems****\\
{{:intro-rec-sys-handbook.pdf|Introduction to Recommender Systems}}\\
{{:facebook_pandora_lead_rise_of_recommendation_engines_--_printout_--_time.pdf|How Computers Know What We Want — Before We Do}}\\
{{:recommender-systems-eml2010.pdf|Recommender Systems}}\\
{{:01423975.pdf|Next Generation Recommender Systems}}\\
{{:p5-l_herlocker.pdf|Evaluating Collaborative Filtering Recommender Systems}}\\
{{:research_paper_recommender_system_evaluation--a_quantitative_literature_survey.pdf|Evaluation of Recommender Systems}}\\
{{:hybridwebrecommendersystems.pdf|Hybrid Recommender Systems}}\\
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*******Spatial Data Mining Systems****\\
{{:ecse408.pdf|Introduction to Spatial Databases}}\\
{{:chapter_10.pdf|Mining Object, Spatial, Multimedia, Text, and Web Data}}\\
{{:soutlier.pdf|A Unified Approach to Spatial Outliers Detection}}\\
{{:coloc.pdf|Discovering Spatial Co-location Patterns}}\\
{{:slide.dvi_-_giscience.pdf|Spatial Data Mining - Accomplishments}}\\
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*******Miscellaneous****\\
{{:glossary.pdf|data mining glossary}}\\
{{:decision_trees.pdf|decision trees}}\\
{{:a_detailed_introduction_to_k-nearest_neighbor_knn_algorithm_god_your_book_is_great_.pdf|K NN algorithm}}\\
{{:bag-of-words.pdf|Bag-of-words representation}}\\
{{:aaac.pdf|Common n-gram classification}}\\
{{:10algorithms-08.pdf|Top 10 algorithms in data mining}}\\