knowledge management data mining text mining

... relationship management (CRM), 1043, 1181, 1189 Data cleaning, 19, 615 Data collection, 1084 Data envelop analysis (DEA), 968 Data management, 559 Data mining, 10 82 Data Mining Tools, ... standard Data Mining problems: regression, classification, clustering, association rule mining, and attribute selection. Getting to know the data is is a very important part of Data Mining, and many data ... commonly used in all forms of Data Mining applications—from bioin- formatics to competition datasets issued by major conferences such as Knowledge Discovery in Databases. New Zealand has several

Ngày tải lên: 04/07/2014, 05:21

... Multimedia Data Mining 58 Data Mining in Medicine Nada Lavra ˇ c, Bla ˇ z Zupan 1111 59 Learning Information Patterns in Biological Databases - Stochastic Data Mining Gautam B. Singh 1137 60 Data Mining ... Determining What Is Interesting Sigal Sahar 603 31 Quality Assessment Approaches in Data Mining Maria Halkidi, Michalis Vazirgiannis 613 32 Data Mining Model Comparison Paolo Giudici 641 33 Data Mining ... S. Yu, Jiawei Han 789 41 Mining High-Dimensional Data Wei Wang, Jiong Yang 803 42 Text Mining and Information Extraction Moty Ben-Dov, Ronen Feldman 809 43 Spatial Data Mining Shashi Shekhar, Pusheng

Ngày tải lên: 04/07/2014, 05:21

... Data Mining: prediction and description. Prediction is often referred to as supervised Data Mining, while descriptive Data Mining includes the unsupervised and visualization aspects of Data Mining. ... Process of Knowledge Discovery in Databases. be determined. This includes finding out what data is available, obtaining additional necessary data, and then integrating all the data for the knowledge ... creating a data set on which discovery will be performed. Having defined the goals, the data that will be used for the knowledge discovery should 1 Introduction to Knowledge Discovery and Data Mining

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... correct data the usefulness of Data Mining and data warehousing is mit- igated. Thus, data cleansing is a necessary precondition for successful knowledge discovery in databases (KDD). 2.2 DATA CLEANSING ... processes are: data warehousing, knowledge discovery in databases, and data/ information quality management (e.g., Total Data Quality Management TDQM). In the data warehouse user community, there ... manual data cleansing and/or relational data integrity analysis. The serious need to store, analyze, and investigate such very large data sets has given rise to the fields of Data Mining (DM) and data

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... data set, containing missing attribute values, is first split into smaller data sets, each smaller data set corresponds to a concept from the original data set. More precisely, every smaller data ... strategies to data with missing attribute values. Proceedings of the Workshop on Foundations and New Directions in Data Mining, associated with the third IEEE International Conference on Data Mining, ... Proceedings of the Workshop on Foundations and New Directions in Data Mining, as- sociated with the third IEEE International Conference on Data Mining, Melbourne, FL, November 1922, 24–30, 2003A. Dardzinska

Ngày tải lên: 04/07/2014, 05:21

... data is distributed. PCA Maximizes Mutual Information on Gaussian Data Now consider some proposed set of projections W ∈ M d  d , where the rows of W are orthonormal, so that the projected data ... of dissimilarity 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 ... example, k’th nearest neighbour) which depends only on dot products of the data. Consider using the same algorithm on transformed data: x → Φ (x) ∈ F , where F is a (possibly infinite dimensional) vector

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... University Summary. Data Mining algorithms search for meaningful patterns in raw data sets. The Data Mining process requires high computational cost when dealing with large data sets. Reducing ... removes attributes from a given data set before feeding it to a Data Mining algorithm. The rationale for this step is the reduction of time required for running the Data Mining algorithm, since the ... ‘modest’ size of 10 attributes. Data- mining algorithms are computationally intensive. Figure 5.1 describes the typical trade-off between the error rate of a Data Mining model and the cost of ob-

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... especially when the data size is large. 6.5 Summary Discretization is a process that transforms quantitative data to qualitative data. It builds a bridge between real-world data- mining applications ... quantitative data flourish, and the learning algorithms many of which are more adept at learning from qualitative data. Hence, discretization has an important role in Data Mining and knowledge discovery. ... Conference on Methodologies for Knowledge Discovery and Data Mining, pages 509–514. Bay, S. D. (2000). Multivariate discretization of continuous variables for set mining. In Pro- ceedings of the

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... entire dataset. However, this method also has an upper limit for the largest dataset that can be processed, because it uses a data structure that scales with the dataset size and this data structure ... Catlett, it assumes that all dataset can fit in the main memory. Chan and Stolfo (1997) suggest partitioning the datasets into several disjointed datasets, so that each dataset is loaded separately ... uncertainties existing in the data collected in industrial systems. 9 Classification Trees 169 9.10.3 Decision Trees Inducers for Large Datasets With the recent growth in the amount of data collected by information

Ngày tải lên: 04/07/2014, 05:21

... knowledge about the parameters of the model M h . The likelihood function, on the other hand, encodes the knowledge about the mechanism underlying the data generation. In our framework, the data ... Our task is to choose one network after observing a sam- ple of data D = {y 1k , ,y vk }, for k = 1, ,n. By Bayes’ theorem, the data D are used to revise the prior probability p(M h ) of each ... of the variables in the data set may avoid the effort to explore a set of not sensible models. For example, we have successfully applied this approach to model survey data (Sebastiani et al.,

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... of data mining. The coverage is intended to be broad rather than deep. Readers are encouraged to consult the references cited. 11.2 Some Definitions There are almost as many definitions of Data Mining ... 2003), and associated with Data Mining are a variety of names: statistical learning, machine learning, reinforcement learning, algorithmic modeling and others. By ? ?Data Min- ing” I mean to emphasize ... from the data. We will be work- ing within the spirit of procedures such as stepwise regression, but beyond allowing the data to determine which predictors are required, we allow the data to determine

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... Moreover, the development of new data mining methods is progressing very quickly, stimulated in part by relatively inexpensive computing power and in part by the Data Mining needs in a variety of ... to Poisson regression (for count data) follows with the deviance used in place of the sum of squares. 11.7.2 Overfitting and Ensemble Methods CART, like most Data Mining procedures, is vulnerable ... with a bootstrap sam- ple of the data having the same number of observations as in the original data set. Then, when each decision is made about subdividing the data, only a random sample of predictors

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... cluster. This algorithm is not suitable for clustering large database data (Fisher, 1987). CLASSIT, an extension of COBWEB for continuous-valued data, unfortunately has similar problems as the COBWEB ... one-dimensional data, while its performance on high di- mensional data sets is unimpressive. The convergence pace of SA is too slow; RBA and TS performed best; and HS is good for high dimensional data. ... complexity in the size of the data set. 2. Its space complexity is O(k + m). It requires additional space to store the data matrix. It is possible to store the data matrix in a secondary memory

Ngày tải lên: 04/07/2014, 05:21

... basket data (Agrawal and Srikant, 1995) • Causes of plan failures (Zaki, 2001) • Web personalization (Mobasher et al., 2002) • Text data (Brin et al., 1997A,Delgado et al., 2002) • Publication databases ... kinds of data, such as: • Census data (Brin et al., 1997A, Brin et al., 1997B) • Linguistic data for writer evaluation (Aumann and Lindell, 2003) • Insurance data (Castelo and Giudici, 2003) ... characteristic for many approaches to association rule mining. Depending on the properties of the database or problem at hand, the frequent itemset mining may be replaced by more efficient variants

Ngày tải lên: 04/07/2014, 05:21

... threshold) and checking them does not need any access to the data. Preprocessing concerns the definition of a mining context D , the mining phase is generally the computation of a theory while ... consider condensed representation mining as a constraint-based Data Mining task (Jeudy and Boulicaut, 2002). It provides not only nice examples of constraint-based mining techniques but also important ... for frequent and closed set mining, or (Garo- falakis et al., 1999) for mining sequences that are both frequent and satisfy a given regular expression in a sequence database. Last but not the

Ngày tải lên: 04/07/2014, 05:21

... language for Data Mining. In Proc. ACM SAC’03 - Data Mining Track, pages 437–444, 2003. R. Meo, G. Psaila, and S. Ceri. An extension to SQL for mining association rules. Data Mining and Knowledge ... type of data is often referred to as relational data. This is as opposed to attribute vector data used by many other unsupervised and supervised Data Mining techniques. In most standard Data Mining ... pattern mining. In Proc. IEEE ICDM’04 (In Press), 2004. J F. Boulicaut. Inductive databases and multiple uses of frequent itemsets: the cInQ ap- proach. In Database Technologies for Data Mining

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... Large Data Bases 1998. 7:163 – 178. Domingos P & Richardson M. Mining the network value of customers. Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining; ... queries. Proceedings of Text -Mining & Link-Analysis Workshop; 2003 August 9; Acapulco, Mexico. Lu Q & Getoor L. Link-based text classification. Proceedings of Text -Mining & Link- Analysis ... Richardson M & Domingos P. Mining knowledge- sharing sites for viral marketing. Pro- ceedings of Eighth International Conference on Knowledge Discovery and Data Mining; 2002 July 28 – Aug 1;

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... attributes that are relevant for the target data mining task (Liu & Motoda 1998; Guyon and Elisseeff 2003). This Subsection assumes the target data mining task is classification – which is the ... of the original attributes, so that the target data mining task becomes easier with the new attributes. This Subsection assumes the target data mining task is classifica- tion – which is the most ... represents a region of the data space with homogeneous data distribution, and the EA was designed to be particularly effective when handling high-dimensional numerical datasets. specification of

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... Feature Extraction, Construction and Selection: a data mining perspective, 393-406. Kluwer. Terano T and Inada M (2002) Data mining from clinical data using interactive evolutionary computation. ... Kluwer. Witten IH and Frank E (2005) Data Mining: practical machine learning tools and techniques . 2nd Ed. Morgan Kaufmann. Wong ML and Leung KS (2000) Data Mining Using Grammar Based Genetic ... establishes its relation to Data- Mining. Specifically, the Reinforcement-Learning problem is defined; a few key ideas for solving it are described; the relevance to Data- Mining is explained; and an

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... view: data mining and knowledge management. Knowledge Management (KM) comprises a range of practices used by organizations to identify, create, represent, and distribute knowledge. Knowledge Management ... eld is being born, called data engineering. One of the essential notions of data engineering is metadata. It is data about data , i.e., a data description of other data. As an example we can ... notion of the book Data Mining and Medical Knowledge Management: Cases and Applica- tions” is knowledge. A number of denitions of this notion can be found in the literature: • Knowledge is the...

Ngày tải lên: 16/08/2013, 16:24