Data Mining: Concepts and Techniques (2nd edition) Jiawei Han and Micheline Kamber Morgan Kaufmann Publishers, Bibliographic Notes for Chapter 2. Data Mining: Concepts and Techniques (2nd edition). Jiawei Han and Micheline Kamber. Morgan Kaufmann Publishers, Bibliographic Notes for Chapter. Information Modeling and Relational Databases, 2nd Edition. Terry Halpin . Data mining: concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei.

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Request PDF on ResearchGate | On Jan 1, , Jiawei Han and others published Data Mining Concepts and Techniques (2nd Edition). Data Mining: Concepts and Techniques This content was uploaded by our users and we assume good faith they have the permission to share this book. Data Mining: Concepts and Techniques (2nd edition) Jiawei Han and Micheline Kamber Morgan Kaufmann Publishers, Bibliographic Notes for Chapter 8.

IEEE Trans. Galhardas, D. Florescu, D. Shasha, E.

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Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. A dynamic index structure for spatial searching. Han and Y. Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases. Harinarayan, A. Rajaraman, and J. Implementing data cubes efficiently. The World According to Wavelets. Peters, Classification Algorithms. John and P. Static versus dynamic sampling for data mining.

Johnson and D. Applied Multivariate Statistical Analysis 5th ed.

Prentice Hall, Discretization of numeric attributes. Kohavi and G.

Data Mining: Concepts and Techniques

Wrappers for feature subset selection. Artificial Intelligence, L Kennedy, Y. Lee, B. Van Roy, C. Reed, and R. Kivinen and H. The power of sampling in knowledge discovery. Liu and H. Motoda eds. Feature Extraction, Construction, and Selection: A Data Mining Per- spective. Liu, F. Hussain, C. Tan, and M. An enabling technique. Data Mining and Knowledge Discovery, 6: Enterprise Knowledge Management: The Data Quality Approach. Morgan Kaufmann, Liu and R. Feature selection and discretization of numeric attributes.

Langley, H. Simon, G. Bradshaw, and J. Scientific Discovery: Computational Explorations of the Creative Processes. MIT Press, Muralikrishna and D. Equi-depth histograms for extimating selectivity factors for multi- dimensional queries.

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Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Data Preparation for Data Mining. Unknown attribute values in induction. Programs for Machine Learning. Management and Technology. Bantam Books, The Field Guide. Digital Press Elsevier , Raman and J. An interactive data cleaning system. Ross and D.

Fast computation of sparse datacubes. Learning and representation change. Siedlecki and J. On automatic feature selection. Pattern Recognition and Artificial Intelligence, 2: Sarawagi and M. Efficient organization of large multidimensional arrays.

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Weiss and N. Predictive Data Mining. Wang, V. Storey, and C. A framework for analysis of data quality research. Knowledge and Data Engineering, 7: Wand and R. Anchoring data quality dimensions in ontological foundations.

Zhao, P. Deshpande, and J. An array-based algorithm for simultaneous multidi- mensional aggregates. Related Papers. By Mojtaba Heravi and Tabassom Azimi. By Senthamarai Kannan. By Jiawei Ong. Data preprocessing for supervised leaning. Mannila, Toivonen, and Verkamo [MTV97] consider frequent episodes in sequences, where episodes are essentially acyclic graphs of events whose edges specify the temporal before-and-after relationship but without timing-interval restrictions.

Garofalakis, Rastogi, and Shim [GRS99] proposed the use of regular expressions as a flexible constraint specification tool that enables usercontrolled focus to be incorporated into the sequential pattern mining process.

The embedding of multidimensional, multilevel information into a transformed sequence database for sequential pattern mining was proposed by Pinto, Han, Pei, et al.

SeqIndex, efficient sequence indexing by frequent and discriminative analysis of sequential patterns, was studied by Cheng, Yan, and Han [CYH05]. Data mining for periodicity analysis has been an interesting theme in data mining.

Lu, Han, and Feng [LHF98] proposed intertransaction association rules, which are implication rules whose two sides are totally ordered episodes with timing-interval restrictions on the events in the episodes and on the two sides. The notion of mining partial periodicity was first proposed by Han, Dong, and Yin, together with a max-subpattern hit set method [HDY99]. Ma and Hellerstein [MH01] proposed a method for mining partially periodic event patterns with unknown periods. A general introduction can be found in Rabiner [Rab89].

Agrawal, C. Faloutsos, and A. Efficient similarity search in sequence databases. In Proc. Aggarwal, J. Han, J. Wang, and P. A framework for clustering evolving data streams. In Proc Int. A framework for projected clustering of high dimensional data streams. On demand classification of data streams.

Agrawal, K. Lin, H. Sawhney, and K. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. Agrawal, G. Psaila, E. Wimmers, and M. Querying shapes of histories. Agrawal and R. Mining sequential patterns. Baldi and S. Bioinformatics: The Machine Learning Approach 2nd ed. Babcock, S. Babu, M. Datar, R. Motwani, and J. Models and issues in data stream systems. Brockwell and R. Introduction to Time Series and Forecasting 2nd ed.

Springer, G. Box, G. Jenkins, and G. Time Series Analysis: Forecasting and Control 3rd ed. Prentice-Hall, A. Baxevanis and B. Babu and J. Continuous queries over data streams. Bettini, X. Sean Wang, and S.

Mining temporal relationships with multiple granularities in time sequences. Cai, D. Clutter, G.

Pape, J. Han, M. Welge, and L. Chen, G. Dong, J. Han, B. Wah, and J. Multi-dimensional regression analysis of timeseries data streams. Chandrasekaran and M. Streaming queries over streaming data. Cong, J. Han, and D. Parallel mining of closed sequential patterns.

Cheng, X. Yan, and J. IncSpan: Incremental mining of sequential patterns in large database. Seqindex: Indexing sequences by sequential pattern analysis. Durbin, S. Eddy, A. Krogh, and G. Cambridge University Press, A. Dobra, M. Garofalakis, J. Gehrke, and R. Processing complex aggregate queries over data streams. Domingos and G. Mining high-speed data streams.

Faloutsos, M. Ranganathan, and Y. Fast subsequence matching in time-series databases. Querying and mining data streams: You only get one look a tutorial. Giannella, J. Pei, X. Yan, and P. Mining frequent patterns in data streams at multiple time granularities. Kargupta, A. Joshi, K. Sivakumar, and Y. Gilbert, Y.

Kotidis, S. Muthukrishnan, and M. Surfing wavelets on streams: One-pass summaries for approximate aggregate queries. Gehrke, F. Korn, and D. On computing correlated aggregates over continuous data streams. Gibbons and Y. New sampling-based summary statistics for improving approximate query answers. Guha, N. Mishra, R. Motwani, and L. O Callaghan. Clustering data streams.

In Proc Symp. Garofalakis, R. Rastogi, and K. Han, G. Dong, and Y.

Data mining : concepts and techniques

Efficient mining of partial periodic patterns in time series database. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. FreeSpan: Frequent patternprojected sequential pattern mining. Hulten, L. Spencer, and P. Mining time-changing data streams. Jones and P. An Introduction to Bioinformatics Algorithms. Karp, C.

Data Mining: Concepts and Techniques

Papadimitriou, and S.A general introduction can be found in Rabiner [Rab89]. IEEE Trans. Mining partially periodic event patterns with unknown periods. Nichols, and B. Frequent Patterns mining in time-sensitive Data Stream www. Cheng, X.