a survey of data mining and knowledge discovery process models and methodologies

Web Mining and Knowledge Discovery of Usage Patterns

... authors of [5] classified the data type as content data, structure data, usage data, and user profile data. M. Spiliopoulou [14] categorized the Web mining into Web usage mining, Web text mining ... and analyze the useful information from the Web data. The authors of [10] claims the Web involves three types of data: data on the Web (content), Web log data (usage) and Web structure data. ... Zaiane, M. Xin, J. Han. Discovering Web Access Patterns and Trends by applying OLAP and Data Mining Technology on Web Logs. In Advances in Digital Libraries, pages 19-29, Santa Barbara, CA,...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 130 doc

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... 1189 Data cleaning, 19, 615 Data collection, 1084 Data envelop analysis (DEA), 968 Data management, 559 Data mining, 1082 Data Mining Tools, 1155 Data reduction, 126, 349, 554, 566, 615 Data transformation, ... Mining, and many data visualization facilities and data preprocessing tools are provided. All algorithms and methods take their input in the form of a single relational table, which can be read from ... graphically throughvisualization of the data and examination of the model (if the model structure is amenable tovisualization). Users can also load and save models. Eibe Frank et al. 66 Weka -A Machine...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 1 pps

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... edition. Ad-vances occurred in areas, such as Multimedia Data Mining, Data Stream Mining, Spatio-temporal Data Mining, Sequences Analysis, Swarm Intelligence, Multi-labelclassiﬁcation and privacy ... in Data Mining, suchas statistical methods for Data Mining, logics for Data Mining, DM query languages,text mining, web mining, causal discovery, ensemble methods, and a great deal more.Part ... identifying valid,novel, useful, and understandable patterns from large datasets. Data Mining (DM)is the mathematical core of the KDD process, involving the inferring algorithmsthat explore the data, ...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 2 pptx

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... Mining 58 Data Mining in MedicineNada Lavraˇc, Blaˇz Zupan 111159 Learning Information Patterns in Biological Databases - Stochastic Data Mining Gautam B. Singh 113760 Data Mining for Financial ... Barko 104156 Mining Time Series Data Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos,EamonnKeogh, Michail Vlachos, Gautam Das 1049Part VII Applications57 Multimedia Data Mining 58 ... PfahringerDepartment of Computer Science,University of Waikato, New ZealandMarco F. RamoniDepartments of Pediatrics and MedicineHarvard University, USAChotirat Ann RatanamahatanaDepartment...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 3 pptx

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... unknownpatterns. The model is used for understanding phenomena from the data, analysis and prediction.The accessibility and abundance of data today makes Knowledge Discovery and Data Mining a matter ... Knowledge Discovery and Data Mining 3Fig. 1.1. The Process of Knowledge Discovery in Databases.be determined. This includes ﬁnding out what data is available, obtainingadditional necessary data, and ... bestunderstanding the phenomena. This tradeoff represents an aspect where theinteractive and iterative aspect of the KDD is taking place. It starts with thebest available data set and later expands and...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 4 ppsx

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... Multimedia Data Mining (Chapter 57). Multimedia data mining, asthe name suggests, presumably is a combination of the two emerging areas: mul-timedia and data mining. Instead, the multimedia data mining ... I.H. and Frank, E., Data Mining: Practical machine learning tools and techniques,Morgan Kaufmann Pub, 2005.Wu, X. and Kumar, V. and Ross Quinlan, J. and Ghosh, J. and Yang, Q. and Motoda, H. and McLachlan, ... such data is that it is unbounded interms of continuity of data generation. This form of data has been termed as data streams to express its owing nature. Mohamed Medhat Gaber, Arkady Zaslavsky,and...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 5 pptx

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... The major areasthat include data cleansing as part of their deﬁning processes are: data warehousing, knowledge discovery in databases, and data/ information quality management (e.g.,Total Data ... investigate such very large data sets hasgiven rise to the ﬁelds of Data Mining (DM) and data warehousing (DW). Withoutclean and correct data the usefulness of Data Mining and data warehousing ... (Ballou and Tayi, 1999, Redman, 1998, Wang et al., 2001) and some tools exist to assist in manual data cleansing and/ or relational data integrityanalysis.The serious need to store, analyze, and...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 6 ppt

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... 464-467.Brachman, R. J., Anand, T., The Process of Knowledge Discovery in Databases — A Human–Centered Approach. In Advances in Knowledge Discovery and Data Min-ing, Fayyad, U. M., Piatetsky-Shapiro, ... Information Patterns and Data Cleaning. InAdvances in Knowledge Discovery and Data Mining, Fayyad, U. M., Piatetsky-Shapiro,G., Smyth, P., & Uthurasamy, R., eds. MIT Press/AAAI Press, 1996.Hamming, ... Very Large Data Bases; 1998 NewYork. 392-403. 32 Jonathan I. Maletic and Andrian MarcusWang, R., Storey, V., & Firth, C. A Framework for Analysis of Data Quality Research, IEEETransactions...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 7 ppsx

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... identiﬁedas a chase algorithm, was also discussed in (Dardzinska and Ras, 200 3A, Dardzinska and Ras, 2003B).Learning missing attribute values from summary constraints was reported in (Wu and Barbara, ... is a Monte Carlo method of handling missingattribute values in which missing attribute values are replaced by many plausiblevalues, then many complete data sets are analyzed and the results are ... 2002,Wu and Barbara, 2002). Yet another approach to handling missingattribute values was presented in (Greco et al., 2000).There is a number of statistical methods of handling missing attribute values,usually...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 8 potx

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... Programs for Machine Learning. Morgan Kaufmann Publishers, SanMateo CA (1993).Schafer J.L. Analysis of Incomplete Multivariate Data. Chapman and Hall, London, 1997.Slowinski R. and Vanderpooten ... variance of the data along the direction n.To characterize the remaining variance of the data, let’s ﬁnd that direction m whichis both orthogonal to n, and along which the projected data again ... the {e a } span the space, we can expand n in terms of them: n =∑d a= 1α a e a , and we’d like to ﬁnd theα a that maximize nCn = n∑ a α a Ce a =∑ a λ a α2 a , subjectto∑ a α2 a = 1...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 9 pdf

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... between each pair of data points in the dataset(note that this measure can be very general, and in particular can allow for non-vectorial data) . Given this, MDS searches for a mapping of the (possibly ... for audio orvideo data) and to make the features more robust. The above features, computed bytaking projections along the n’s, are ﬁrst translated and normalized so that the signal data has ... (Basilevsky,1994, Tipping and Bishop, 199 9A) . Suppose thatΨ=σ21, that the d −dsmallesteigenvalues of the model covariance are the same and are equal toσ2, and that thesample covariance...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 10 ppt

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... Linear EmbeddingLocally linear embedding (LLE) (Roweis and Saul, 2000) models the manifold bytreating it as a union of linear patches, in analogy to using coordinate charts to pa-rameterize a ... defined asthe sum of the weights of the removed arcs. Given the mapping of data to graph de-fined above, a cut defines a split of the data into two clusters, and the minimum cutencapsulates the ... Sdis of full rank. This canbe seen as follows: since the rank of Z is d and since the rank of a product of matri-ces is bounded above by the rank of each, we have that d= rank(Z)=rank(YPY...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 11 pdf

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... Reduction and Feature SelectionBarak Chizi1 and Oded Maimon1Tel-Aviv UniversitySummary. Data Mining algorithms search for meaningful patterns in raw data sets. The Data Mining process requires ... the dimensionality of the data, it holds out the possibility of more effective& rapid operation of data mining algorithms (i.e. Data Mining algorithms can beoperated faster and more effectively ... usingcross- validation (a wrapper approach) to estimate the accuracy of tables (and hencefeature sets). The MDL approach was shown to be more efﬁcient than, and performas well as, as cross- validation.An...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 12 ppsx

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... a survey of variable selection. Suppose is Y a variable of interest, and X1, ,Xpis a set of potential explanatory variables or predictors,are vectors of n observations. The problem of variable ... 975.3.4 Factor Analysis (FA)Like PCA, factor analysis (FA) is also a linear method, based on the second-order data summaries. First suggested by psychologists, FA assumes that the measuredvariables ... using a search strategy and cross validation to estimate accuracy. For each instance in back-ward the trainingset, RC ﬁnds its nearest neighbour of the same class and removes those features...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 13 pot

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... value range of the quantitative data. It then as-sociates a qualitative value to each interval. A cut point is a value among the quanti-tative data where an interval boundary is located by a ... Time-insensitivediscretization only uses the stationary pro-perties of the quantitative data. 9. Ordinal vs. Nominal. Ordinal discretization transforms quantitative data intoordinal qualitative data. It aims at taking ... referred to as categorical data, are data that can beplaced into distinct categories. Qualitative data sometimes can be arrayed in a mean-ingful order. But no arithmetic operations can be applied...