data information and knowledge examples

... Trang 2Advanced Information and Knowledge ProcessingAlso in this series Gregoris Mentzas, Dimitris Apostolou, Andreas Abecker and Ron Young Knowledge Asset Management 1-85233-583-1 ... Evolutionary Algorithms and Applications 1-85233-836-9 Nikhil R Pal and Lakhmi Jain (Eds) Advanced Techniques in Knowledge Discovery and Data Mining 1-85233-867-9 Amit Konar and Lakhmi Jain Cognitive ... back-ground information for readers unfamiliar with information assurance or withdata mining and machine learning In Chap 2, Clay Shields provides an in-troduction to information assurance and identifies

Ngày tải lên: 07/09/2020, 13:19

... Multivariate Data Chapman and Hall, London, 1997. Slowinski R and Vanderpooten D A generalized definition of rough approximations based on similarity IEEE Transactions on Knowledge and Data Engineering ... incomplete information databases ACM Transactions on Database Systems 4 (1979), 262–296. Lipski W Jr On databases with incomplete information Journal of the ACM 28 (1981) 41– 70 Little R.J.A and Rubin ... Newsletter 4 (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 Dis-covery, Aix-en-Provence,

Ngày tải lên: 04/07/2014, 05:21

... Karhunen, and E Oja Independent Component Analysis Wiley, 2001. Y LeCun and Y Bengio Convolutional networks for images, speech and time-series In M Arbib, editor, The Handbook of Brain Theory and Neural ... minimal erer-ror rate ε∗ and costs h ∗to be derived) On some occasions, one might prefer using an inferior O Maimon, L Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI ... identify features in the data-set as important, and discard any other feature as irrelevant and redundant information Since feature selection re-duces the dimensionality of the data, it holds out the

Ngày tải lên: 04/07/2014, 05:21

... Review, (11): 227– 253, 1997 Elder, J.F and Pregibon, D “A Statistical perspective on knowledge discovery in databases” In Advances in Knowledge Discovery and Data Mining, Fayyad, U Piatetsky-Shapiro, ... pp 178-196, 2002 Maimon, O and Rokach, L., Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications, Series in Machine Perception and Artificial In-telligence ... Letters, 27(14): 1619–1631, 2006, Elsevier Averbuch, M and Karson, T and Ben-Ami, B and Maimon, O and Rokach, L., Context-sensitive medical information retrieval, The 11th World Congress on Medical

Ngày tải lên: 04/07/2014, 05:21

... analysis, and other data-mining tasks (Hawkins, 1980, Barnett and Lewis, 1994, Ruts and Rousseeuw, 1996, Fawcett and Provost, 1997, Johnson et al., 1998, Penny and Jolliffe, 2001,Acuna and Rodriguez, ... Rousseeuw, 1990, Ng and Han, 1994, Ramaswamy et al., 2000, Barbara and Chen, 2000, Shekhar and Chawla, 2002, Shekhar and Lu, 2001, Shekhar and Lu, 2002, Acuna and Rodriguez, 2004). Hu and Sung (2003) ... 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.

Ngày tải lên: 04/07/2014, 05:21

... When data is limited, it is common practice to re-sample the data, that is, partition the data into training and test sets in different ways An inducer is trained and tested for each partition and ... is provided Random sub-sampling and n-fold cross-validation are two common methods of re-sampling In random subsampling, the data is randomly partitioned into disjoint training and test sets ... regression and probability estimation, classification is one of the most studied models, possibly one with the greatest practical relevance The potential ben-O Maimon, L Rokach (eds.), Data Mining and Knowledge

Ngày tải lên: 04/07/2014, 05:21

... programming (Duda and Hart, 1973, Bennett and Mangasarian, 1994), linear discriminant analysis (Duda and Hart, 1973, Friedman, 1977, Sklansky and Wassel, 1981, Lin and Fu, 1983, Loh and Vanichsetakul, ... 1S −σa i ∈dom1(a i )ANDy=c2S σy =c 2S  This measure was extended in (Utgoff and Clouse, 1996) to handle target at-tributes with multiple classes and missing data values Their results ... above, and others, have been conducted by several researchers during the last thirty years, such as (Baker and Jain, 1976, BenBassat, 1978, Mingers, 1989, Fayyad and Irani, 1992, Buntine and Niblett,

Ngày tải lên: 04/07/2014, 05:21

... 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 into the memory and ... 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 ... 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

... Classifier for Data Mining, Proc 22nd Int Conf Very Large Databases, T M Vijayaraman and Alejandro P Buchmann and C Mohan and Nandlal L Sarda (eds), 544-555, Morgan Kaufmann, 1996 Sklansky, J and Wassel, ... 2005b, pp 131–158 Rokach, L and Maimon, O., Clustering methods, Data Mining and Knowledge Discovery Handbook, pp 321–352, 2005, Springer Rokach, L and Maimon, O., Data mining for improving the ... Tree Construction of Large Datasets,Data Mining and Knowledge Discovery, 4, 2/3) 127-162, 2000 Gelfand S B., Ravishankar C S., and Delp E J., An iterative growing and pruning algo-rithm for classification

Ngày tải lên: 04/07/2014, 05:21

... simple database of seven cases, and the frequencies n 3 jk The full joint distribution is defined by the parametersθ3 jk, and the parametersθ1kandθ2k that specify the marginal distributions of Y1and ... regarded as a random vector, with a prior density p(θh) that encodes any prior knowledge about the parameters of the model M h The likelihood function, on the other hand, encodes the knowledge about ... j  are independent for i  = i and j = j  These as-sumptions are known as global and local parameter independence (Spiegelhalter and Lauritzen, 1990), and are valid only under the assumption

Ngày tải lên: 04/07/2014, 05:21

... and decision makers with useful information in a compact and understand- able format. Data are expected to improve the understanding of institutions, busi- nesses, and citizens of the current state ... in the data set (Yu et al., 2002). 10.6 Data Mining Applications Bayesian networks have been used by us and others as knowledge discovery tools in a variety of fields, ranging from survey data analysis ... the country and play a key role in political decisions. But the size and structure of this fast-growing databases pose the challenge of how effectively extracting and presenting this information

Ngày tải lên: 04/07/2014, 05:21

... (Sutton and Barto, 1999, Cristianini and Shawe-Taylor, 2000, Witten and Frank, 2000,Hand et al., 2001,Hastie et al., 2001,Breiman, 2001b,Dasu and Johnson, 2003), and associated with Data Mining ... which are the values one wants to estimate from the data on hand However, in repeated independent random samples (or random realizations of the data), the fitted values will vary less Conversely, ... increases, the space that needs to be filled with data goes up as a power function So, the demand for data increases rapidly, and the risk is that the data will be far too sparse to get a meaningful

Ngày tải lên: 04/07/2014, 05:21

... The data are first segmented left from right and then for the two resulting par- titions, the data are further segmented separately into an upper and lower part. The upper left partition and the ... (e.g., 5). “Random forests” is one powerful approach exploiting these ideas. It builds on CART, and will generally fit the data better than standard regression models or CART 13 “Bagging” stands for ... distinction between the more effective and the less effective Data Mining procedures is how overfitting is handled. Finding new and improved ways to fit data is often quite easy. Finding ways to

Ngày tải lên: 04/07/2014, 05:21

... BerkDasu, T., and T Johnson (2003) Exploratory Data Mining and Data Cleaning New York: John Wiley and Sons Christianini, N and J Shawe-Taylor (2000) Support Vector Machines Cambridge, England: Cambridge ... Multivariate Analysis New York: John Wiley and Sons Hand, D., Manilla, H., and P Smyth (2001) Principle of Data Mining Cambridge, Mas-sachusetts: MIT Press Hastie, T.J and R.J Tibshirani (1990) Generalized ... subset of training examples, called support vectors. There are numerous books and tutorial papers on the theory and practice of SVM (Scholkopf and Smola 2002, Cristianini and Shawe-Taylor 2000,

Ngày tải lên: 04/07/2014, 05:21

... Vapnik V.N Extracting support data for a given task In Fayyad U.M and Uthurusamy R., Editors, Proceedings, First International Conference on Knowledge Discovery and Data Mining AAAI Press, Menlo ... inference, and the model selection is accomplished by maximizing the marginal likelihood (i.e., evidence) Law and Kwok (2000) and Chu (2003) provide iterative parameter updating formulas, and report ... C-SVM andν-SVM classifiers 12.5 Extensions and Application Kernel algorithms have solid foundations in statistical learning theory and functional analysis, thus, kernel methods combine statistics and

Ngày tải lên: 04/07/2014, 05:21

... S=+k i=1C i and C i ∩C j= /0 for i = j Consequently, any instance in S belongs to exactly one and only one subset. O Maimon, L Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ... Paradigms and Methods, Carbonell, J G (ed.), The MIT Press, Boston, MA, 1990, 235–282 Chan C.C and Grzymala-Busse J.W On the attribute redundancy and the learning programs ID3, PRISM, and LEM2 ... 27–39. Trang 6Holland J.H., Holyoak K.J., and Nisbett R.E Induction Processes of Inference, Learning,and Discovery, MIT Press, Boston, MA, 1986. Japkowicz N Learning from imbalanced data sets: a comparison

Ngày tải lên: 04/07/2014, 05:21

... cluster in C1; and d be the number of pairs of instances that are assigned to different clusters in C and C The quantities a and d Trang 9can be interpreted as agreements, and b and c as disagreements ... disagreements The Rand index isdefined as: a + b + c + d The Rand index lies between 0 and 1 When the two partitions agree perfectly, the Rand index is 1 A problem with the Rand index is that its ... ) measure the similarity and distance of the vectors x jand x k The C-Criterion The C-criterion (Fortier and Solomon, 1996) is an extension of Condorcet’s criterion and is defined as: ∑ C i ∈C

Ngày tải lên: 04/07/2014, 05:21

... (Farley and Raftery, 1998): an “E-step”, in which the conditional expectation of the complete data likelihood given the observed data and the current parameter estimates is computed, and an “M-step”, ... Bernoulli, Poisson, and log-normal distributions (Cheese- man and Stutz, 1996). Other well-known density-based methods include: SNOB (Wallace and Dowe, 1994) and MCLUST (Farley and Raftery, 1998). ... that both Mishra and Raghavan (1994) and Al-Sultan and Khan (1996) have used relatively small data sets in their experimental studies. In summary, only the K-means algorithm and its ANN equivalent,

Ngày tải lên: 04/07/2014, 05:21

... 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 ... the data is is a very important part of Data Mining, and many data visualization facilities and data preprocessing tools are provided. All algorithms and methods take their input in the form ... 940, 1004 Multimedia, 1081 database, 1082 indexing and retrieval, 1082 presentation, 1082 data, 1084 data mining, 1081, 1083, 1084 indexing and retrieval, 1083 Multinomial distribution, 184 Multirelational Data Mining,...

Ngày tải lên: 04/07/2014, 05:21

... Rokach Editors Data Mining and Knowledge Discovery Handbook Second Edition 123 Contents 1 Introduction to Knowledge Discovery and Data Mining Oded Maimon, Lior Rokach 1 Part I Preprocessing Methods 2 Data ... by today’s abundance of data. Knowledge Discovery in Databases (KDD) is the process of identifying valid, novel, useful, and understandable patterns from large datasets. Data Mining (DM) is the ... neural networks, and evolutionary algorithms. Parts five and six present supporting and advanced methods in Data Mining, such as statistical methods for Data Mining, logics for Data Mining, DM...

Ngày tải lên: 04/07/2014, 05:21