Data Mining e Scoperta di Conoscenza

Libri di testo:

TM = Tom Mitchell, Machine Learning. McGraw Hill, 1997.

WF = Ian Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2000

PC = R. Duda, P. Hart, D. Stork. Pattern Classification, Wiley, 2001.

HK = J. Han and M. Kamber, Data Mining Techniques, Morgan-Kaufman, 2000.

MS = D. Hand, H. Mannila, P. Smyth.Principles of Data Mining, MIT Press, 2001.

HA = S. Haykin, Neural Networks, Prentice Hall, 1999.

PY = D. Pyle, Data Preparation for Data Mining, Morgan-Kaufman, 1999.

Schedule:

Data	Argomenti	Materiale	Docente	Approfondimenti
13 ottobre 2004	Introduzione. Caratterizzazione del Knowledge discovery come processo.	Lucidi [pdf ] Dispense [pdf]	Manco	HK, cap.1; MS, cap. 1 U. Fayyad and others, "From Data Mining to Knowledge discovery in Databases". Applicazioni di Data Mining S. Chaudhuri, U. Dayal, V. Ganti. "Database technology for Decision Support Systems".
14 ottobre 2004	Data Preprocessing. Statistiche descrittive. Pulizia e trasformazione dei dati. Discretizzazione.	Lucidi [pdf ]	Manco	HK, cap.2-3; MS cap.2-3; PY. Codd, "Providing OLAP to the user analyst: An IT mandate". S. Chaudhuri, U. Dayal, "An Overview of Data warehouse and OLAP technology". E. Galhardas and others, "Declarative data cleaning: Languages, models and algorithms". M .Hernandez, S. Stolfo, "Real-world data is dirty". H. Lee and others, "Cleansing data for mining and warehousing".
15 ottobre 2004	Esercitazione su data preprocessing. Un caso di studio.	Lucidi [pdf ] Dataset [arff]	Folino	Weka è disponibile da questo sito.
19 ottobre 2004	Analisi delle Componenti Principali.	Dispense [pdf]	Manco	PC, cap. 3.8.1; HK cap. 3.4.3;MS cap.3.6. AA. VV., The New Jersey Data Reduction Report. M. Wall, A. Rechsteiner, L. Rocha, Singular Value Decomposition and Principal Component Analysis.
20 ottobre 2004	Concept Learning. Apprendimento induttivo e bias induttivo	Lucidi [pdf]	Manco	TM, cap. 2.
26 ottobre 2004	L'algoritmo Candidate Elimination. Inductive Bias		Folino	TM, cap. 2. H. Hirsch. "Polynomial-Time Learning with Version Spaces".
27 ottobre 2004	Esercitazione su concept learning	Dispense [pdf]	Folino
29 ottobre 2004	Discretizzazione supervisionata: l'algoritmo ChiMerge. Alberi di Decisione. L'algoritmo CHAID	algoritmo ChiMerge [java]	Folino	H. Liu and others, "Discretization: an enabling technique". J. Dougherty, R. Kohavi, M. Sahami, "Supervised and Unsupervised discretization of Continuous features". R. Holte, "Very simple classification Rules perform well on most commonly used datasets".
04 novembre 2004	Decision Tree Learning	Lucidi [pdf] Dispense [pdf]	Manco	HK, cap. 7, TM, cap. 3. M. Mehta, R. Agrawal, J. Rissanen, "SLIQ: A scalable Decision-Tree classifier for Data Mining" Freund, Y., Mason, L, "The alternating decision tree learning algorithm". L. Breiman, "Random Forests". J. Gehrke, R. Ramakrishnan, V. Ganti, "RainForest: A Framework for Large Decision Tree Construction of Large DataSets" Lim, Loh, Shih, "An Empirical Comparison of Decision Trees and Other Classification Methods"
09 novembre 2004	Decision Trees. Model Evaluation	Lucidi [pdf]	Manco	T. Fawcett, "ROC Graphs: Notes and practical Considerations for data mining researchers". C. Ferri, P. Flach, J-H. Orallo, "Lerning Decision Trees using the area under the ROC Curve". E. Frank et al. "Using Model Trees for Regression". A. Moore, M. Lee, "Efficient Algorithms for minimizing Cross-Validation Error".
10 novembre 2004	Neural Networks	Lucidi [pdf]	Manco	TM, cap. 3; HA cap.3-4;PC cap.5.1-5.5,6.1-6.8. A.K. Jain, J. Mao, "A tutorial on Neural Networks".IEEE Computer, march 1996 B.D. Ripley, "Pattern Recognition via Neural Networks". Y. Freund, R. Schapire, "Large Margin Classification using the Perceptron Algorithm". B.D. Ripley, "Can Statistical Theory Help us use Neural Networks Better?" C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition". J. Platt, "Fast Training of Support Vector Machines using Sequential Minimal Optimization". M.J.J. Orr, "Introduction to Radial Basis Function Networks".
16 novembre 2004	Apprendimento Bayesiano	Lucidi [pdf]	Manco	TM, cap. 3; HA cap.3-4;PC cap.5.1-5.5,6.1-6.8. J. Elder, J. Pregibon, "A statistical Perspective on Knowledge Discovery in Databases". G. John, P. Langley, "Estimating Continuous Distributions in Bayesian Classifiers". A. Mccallum, K. Nigam, "A Comparison of Event Models for Naive Bayes Text Classification". P. Langley et al. "An Analysis of Bayesian Classifiers". Webb. Boughon, Wang, "Not so Naive Bayes". J. Provost, "Naive Bayes vs Rule Learning for E-mail classification". R. Kohavi, "Scaling up the accuracy of naive-Bayes classifiers: a decision tree hybrid".
17 novembre 2004	Instance-Based Learning.	Lucidi [pdf]	Manco	TM, cap. 8; PC cap.5.1-5.5,6.1-6.8. D. Aha, D. Kibler, M. Albert, "Instance-Based Learning Algorithms". C. Atkenson et al. "Locally Weighted Learning". E. Frank, M. Hall, B. Pfharinger, "Locally Based Naive Bayes". P. Langley, W. Iba, "Average-Case Analysis of a Nearest Neighbor Algorithm". W. Emde, D. Wettscherek, "Relational Instance-Based Learning".
18 novembre 2004	Reti Bayesiane Metaclassificazione	Lucidi [pdf] Lucidi [pdf]	Folino	PC cap.2.1. R. Bouckaert, "Bayesian Network Classifiers in Weka". D. Heckerman, "A Tutorial on Learning with Bayesian Networks". R. Freund, "The Boosting approach to Machine Learning". L. Breiman , "Bagging predictors" Website su Ensemble Learning
23 novembre 2004	Discussione Progetti. Introduzione al Clustering	Lucidi [pdf] Progetti	Manco	HK. cap.9. A. K. Jain and R. C. Dubes. "Data Clustering: A review". L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 1990. R. Ng and J. Han. Efficient and effective clustering method for spatial data mining. Fayyad U., Reina C., Bradley P. S. "Initialization of Iterative Refinement Clustering Algorithms", A. Strehl, J. Gosh, R. Mooney, "Impact of Similarity Measures on Web Document Clustering".
24 Novembre 2004	Clustering Basato su densità	Lucidi [pdf]	Manco	HK. cap.9. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander. "Optics: Ordering points to identify the clustering structure".
25 Novembre 2004	Clustering gerarchico	Lucidi [pdf]	Manco	HK. cap.9; PC cap. 10.9;.SL, cap. 14 D. Fisher. "Knowledge acquisition via incremental conceptual clustering". T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH : an efficient data clustering method for very large databases. S. Guha, R. Rastogi, and K. Shim: "ROCK: A robust clustering algorithm for categorical Data". S. Guha, R. Rastogi, and K. Shim. Cure: An efficient clustering algorithm for large databases.
30 Novembre 2004	Clustering Model-Based. Altri approcci al Clustering	Lucidi [pdf]	Manco	HK. cap.9; PC cap. 3.14;.SL, cap. 14 G. J. McLachlan and K.E. Bkasford. Mixture Models: Inference and Applications to Clustering. John Wiley and Sons, 1988. T. Moon, The EM Algorithm. P. Cheeseman, J. Stoutz, "Bayesian Classification (Autoclass): Theory and Results". I. V. Cadez, and others, "Model-Based Clustering and visualization of navigation patterns on the web". D. Heckerman; C. Meek; B. Thiesson "Accelerating EM for large databases". V. Ganti, J. Gehrke, R. Ramakrishnan "CACTUS: Clustering Cateogorical Data" R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. "Automatic subspace clustering of high dimensional data for data mining applications".
1 Dicembre 2004	Regole associative	Lucidi [pdf]	Manco	HK. cap.6; SL cap. 14 R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. H. Mannila, H. Toivonen, and A. I. Verkamo. Efficient algorithms for discovering association rules.
2 dicembre 2004	L'algoritmo Apriori	Codice Apriori (con Prefix-Tree)	Manco	R. Agrawal and R. Srikant. Fast algorithms for mining association rules. Ashoka Savasere, Edward Omiecinski, Shamkant B. Navathe: An Efficient Algorithm for Mining Association Rules in Large Databases. J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules. H. Toivonen. Sampling large databases for association rules. . (citeseer) R. Srikant and R. Agrawal. Mining generalized association rules. R. Srikant and R. Agrawal. Mining quantitative association rules in large relational tables. S. Brin, R. Motwani, and C. Silverstein. Beyond market basket: Generalizing association rules to correlations. D. Tsur, and others. Query flocks: A generalization of association-rule mining. (citeseer) Y. Aumann and Y. Lindell. A Statistical Theory for Quantitative Association Rules. J. Han, J. Wang, Y. Lu, and P. Tzvetkov, “Mining Top-K Frequent Closed Patterns without Minimum Support”. A. Savasere, E. Omiecinski, S. B. Navathe, Mining for Strong Negative Associations in a Large Database of Customer Transactions. E. Omiecinski. Alternative Interest Measures for Mining Associations. R. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules..
7 dicembre 2004	Estensioni. L'algoritmo FP-Growth	Lucidi [pdf]	Folino	J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for generation of frequent itemsets. (citeseer) Zaki and Hsiao. CHARM: An Efficient Algorithm for Closed Itemset Mining. R. J. Bayardo. Efficiently mining long patterns from databases. (citeseer) Y. Xu, J. X. Yu, G. Liu, H. Lu, From Path Tree To Frequent Patterns: A Framework for Mining Frequent Patterns. G. Liu, H. Lu, W. Lou, J. X. Yu , On Computing, Storing and Querying Frequent Patterns. B. Goethals, M. Zaki: FIMI: Workshop on Frequent Itemset Mining Implementations (An Introduction).
9 dicembre 2004	Patterns Sequenziali. Serie temporali	Lucidi [pdf]	Folino	Time Series Data Mining archive, mantenuto da Eamonn Keogh. R. Agrawal, C. Faloutsos, A. Swami, "Efficient Similarity Search in Sequence databases". R. Srikant, R. Agrawal, "Finding Sequential Patterns".