discovery of clinical data mining and why practitioners should do it

... Invocation Methods and Algorithms Weka contains a comprehensive set of useful algorithms for a panoply of Data Mining tasks These include tools for data engineering (called “filters”), algorithms for attribute ... computing random projections, and processing time series data Unsupervised instance filters transform sparse instances into non-sparse instances and vice versa, randomize and resample sets of instances, ... Zealand However, the machine learning methods and data engineering capability it embodies have grown so quickly, and so radically, that the workbench is now commonly used in all forms of Data Mining...

Ngày tải lên: 04/07/2014, 05:21

... Data Mining and Knowledge Discovery Handbook Second Edition Oded Maimon · Lior Rokach Editors Data Mining and Knowledge Discovery Handbook Second Edition 123 Editors Prof Oded Maimon ... theories, methodologies, trends, challenges and applications of Data Mining into a coherent and unified repository This handbook provides researchers, scholars, students and professionals with a comprehensive, ... the data mining research and development communities The field of data mining has evolved in several aspects since the first edition Advances occurred in areas, such as Multimedia Data Mining, Data...

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... Maria M Abad Software Engineering Department, University of Granada, Spain Ajith Abraham Center of Excellence for Quantifiable Quality of Service Norwegian University of Science and Technology, ... of Computer Science, University of Regina, Canada Nitesh V Chawla Department of Computer Science and Engineering, University of Notre Dame, USA XVI List of Contributors Ping Chen Department of ... University of Calabria, Italy Richard S Segall Arkansas State University, Department of Computer and Info Tech., Jonesboro, AR 72467-0130,USA Shashi Shekhar Institute of Technology, University of...

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... understanding phenomena from the data, analysis and prediction The accessibility and abundance of data today makes Knowledge Discovery and Data Mining a matter of considerable importance and necessity ... amounts of data with less variability in data types and reliability Since the information age, the accumulation of data has become easier and less costly It has been estimated that the amount of stored ... doubles every twenty months Unfortunately, as the amount of electronically stored information increases, the ability to understand and make use of it does not keep pace with its growth Data Mining...

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... emergence of data streams The distinctive characteristic of such data is that it is unbounded in terms of continuity of data generation This form of data has been termed as data streams to express its ... commercial software for data mining, text mining, and web mining The selected software are compared with their features and also applied to available data sets Screen shots of each of the selected software ... Knowledge Discovery and Data Mining 15 Rokach, L., Maimon, O., Data Mining with Decision Trees: Theory and Applications, World Scientific Publishing, 2008 Witten, I.H and Frank, E., Data Mining: ...

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... application to quality (Levitin and Redman, 1995) the data acquisition and data usage cycles contain a series of activities: assessment, analysis, adjustment, and discarding of data Although it is not ... with the process management issues from data quality perspective, others with the definition of data quality The later category is of interest here In the proposed model of data life cycles with ... integrated the data cleansing process with the data life cycles, this series of steps would define it in the proposed model from the data quality perspective In the same framework of data quality, (Fox...

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... on Knowledge Discovery and Data Mining; 2000 August 20-23; Boston, MA 290-294 Levitin, A & Redman, T A Model of the Data (Life) Cycles with Application to Quality, Information and Software Technology ... Methods, Data Mining and Knowledge Discovery Handbook, Springer, pp 321-352 Simoudis, E., Livezey, B., & Kerber, R., Using Recon for Data Cleaning In Advances in Knowledge Discovery and Data Mining, ... Jerzy W Grzymala-Busse1 and Witold J Grzymala-Busse 2 University of Kansas FilterLogix Inc Summary In this chapter methods of handling missing attribute values in Data Mining are described These...

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... that for every specific data set the best method of handling missing attribute values should be chosen individually, using as the criterion of optimality the arithmetic mean of many multi-fold cross ... 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, ... (Allison, 2002, Little and Rubin, 2002, Schikuta, 1996), such as maximum likelihood and the EM algorithm Recently multiple imputation gained popularity It is a Monte Carlo method of handling missing...

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... Knowledge and Data Engineering 12 (2000) 331– 336 Stefanowski J Algorithms of Decision Rule Induction in Data Mining Poznan University of Technology Press, Poznan, Poland (2001) Stefanowski J and Tsoukias ... Schafer J.L Analysis of Incomplete Multivariate Data Chapman and Hall, London, 1997 Slowinski R and Vanderpooten D A generalized definition of rough approximations based on similarity IEEE Transactions ... (2002) 21 – 30 Wu X and Barbara D Modeling and imputation of large incomplete multidimensional datasets Proc of the 4-th Int Conference on Data Warehousing and Knowledge Discovery, Aix-en-Provence,...

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... Eigendecomposition o ˜ Suppose that Kmm has rank r < m Since it s positive semidefinite it is a Gram matrix ˜ and can be written as K = ZZ where Z ∈ Mmr and Z is also of rank r (Horn and Johnson, ... algorithms with multidimensional scaling (MDS), which arose in the behavioral sciences (Borg and Groenen, 1997) MDS starts with a measure of dissimilarity between each pair of data points in the dataset ... non-linearly on the data) , and this can severely limit the usefulness of the approach Several versions of nonlinear PCA have been proposed (see e.g (Diamantaras and Kung, 1996)) in the hope of overcoming...

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... number of connected components in the graph, and in fact the spectrum of a graph is the union of the spectra of its connected components; and the sum of the eigenvalues is bounded above by m, with ... removing a set of arcs, the cut is defined as the sum of the weights of the removed arcs Given the mapping of data to graph defined above, a cut defines a split of the data into two clusters, and the minimum ... smoothness of the eigenfunctions and on the distribution of the data, the eigendecomposition performed by LLE can be shown to coincide with the eigendecomposition of the squared Laplacian (Belkin and...

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... reduces 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 be operated faster and more effectively ... practice, the exact tradeoff curve of Figure 5.1 is seldom known, and generating it might be computationally prohibitive The objective of dimension reduction in Data Mining domains is to identify ... Introduction Data Mining algorithms are used for searching meaningful patterns in raw data sets Dimensionality (i.e., the number of data set attributes or groups of attributes) constitutes a serious...

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... Proceedings of the First International Conference on Knowledge Discovery and Data Mining AAAI Press, 1995 Caruana, R and Freitag, D Greedy attribute selection In Machine Learning: Proceedings of the ... Cherkauer, K J and Shavlik, J W Growing simpler decision trees to facilitate knowledge discovery In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining AAAI ... 2002 Maimon, O and Rokach, L., Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications, Series in Machine Perception and Artificial Intelligence - Vol 61, World...

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... quantitative data into qualitative data Data Mining applications often involve quantitative data However, there exist many learning algorithms that are primarily oriented to handle qualitative data (Kerber, ... as it is usually applied in data mining is best defined as the transformation from quantitative data to qualitative data In consequence, we will refer to data as either quantitative or qualitative ... University, Australia geoff.webb@infotech.monash.edu Department of Computer Science University of Vermont, USA xwu@cs.uvm.edu Summary Data- mining applications often involve quantitative data However,...

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... 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 ... unsuitable for high-dimensional data sets and for arbitrary data sets without prior knowledge of the underlying data distribution (Papadimitriou et al., 2002) Within the class of non-parametric outlier ... 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 where quantitative...

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... intrusion detection algorithm based on hypothesis testing of command transition probabilities,” In Proceedings of the 4th International Conference on Knowledge Discovery and Data- mining (KDD98), 189–193, ... number of features and n is the sample size Hence, it is not an adequate definition to use with very large datasets Moreover, this definition can lead to problems when the data set has both dense and ... methods is the use of biased sampling Kollios et al (2003) investigate the use of biased sampling according to the density of the data set to speed up the operation of general data- mining tasks, such...

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... nominal, it is useful to denote by dom(ai ) = {vi,1 , vi,2 , , vi,|dom(ai )| } its domain values, where |dom(ai )| stands for its finite cardinality In a similar way, dom(y) = {c1 , , c|dom(y)| ... number of randomly drawn training examples and a reasonable amount of computation” (Mitchell, 1997) We use the following formal definition of PAC-learnable adapted from (Mitchell, 1997): Definition ... subsampling, the data is randomly partitioned into disjoint training and test sets several times Errors obtained from each partition are averaged In n-fold cross-validation, the data is randomly split into...

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... part of the stored data According to Fayyad et al (1996) the explicit challenges for the data mining research community are to develop methods that facilitate the use of Data Mining algorithms ... task of efficient Data Mining into mission impossible Managing and analyzing huge data warehouses requires special and very expensive hardware and software, which often causes a company to exploit ... several terabytes of raw data every one to two years However, the availability of an electronic data repository (in its enhanced form known as a data warehouse”) has created a number of previously...

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... statistic is distributed as χ with degrees of freedom equal to: (dom(ai ) − 1) · (dom(y) − 1) 9.3.6 DKM Criterion The DKM criterion is an impurity-based splitting criterion designed for binary class ... KS(ai , dom1 (ai ), dom2 (ai ), S) = σai ∈dom1 (ai ) AND y=c1 S σy=c1 S − σai ∈dom1 (ai ) AND y=c2 S σy=c2 S This measure was extended in (Utgoff and Clouse, 1996) to handle target attributes with ... the ROC curve It is important to note that unlike impurity criteria, this criterion does not perform a comparison between the impurity of the parent node with the weighted impurity of the children...

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... ), dom2 (a j ), S) = σa ∈dom i S (ai ) AND a j ∈dom1 (a j ) |S| + σa ∈dom i S (ai ) AND a j ∈dom2 (a j ) |S| When the first split refers to attribute and it splits dom(ai ) into dom1 (ai ) and dom2 ... unknown, that is, instead of using the splitting criteria Δ Φ (ai , S) it uses Δ Φ (ai , S − σai =? S) On the other hand, in case of missing values, the splitting criteria should be reduced proportionally ... (ai ) The alternative split refers to attribute a j and splits its domain to dom1 (a j ) and dom2 (a j ) The missing value can be estimated based on other instances (Loh and Shih, 1997) On the...

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