voice and data communications handbook 5th edition

... 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) ... the data- mining methods, also called distance-based methods. These methods are usu- ally based on local distance measures and are capable of handling large databases (Knorr and Ng, 1997, Knorr and

Ngày tải lên: 04/07/2014, 05:21

... in large data sets,” IEEE Transactions on Knowl- edge and Data Engineering, 15 (5), 1170-1187, 2003. Liu H., Shah S., Jiang W., ”On-line outlier detection and data cleaning,” Computers and Chemical ... 1994, Lu and Reynolds, 1999, Runger and Willemain, 1995, Apley and Shi, 1999)), and to parameter-free methods, where the model parameters are only implicitly derived, if at all (Montgomery and Mas- ... good estimation for data location and data shape if it is not contaminated by outliers. When the database 7 Outlier Detection 121 is contaminated, those parameters may deviate and significantly affect

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 ... presents basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and chal- lenges for data mining tasks.

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, ... i ∈dom 1 (a i )AND y=c 2 S     σ y=c 2 S        This measure was extended in (Utgoff and Clouse, 1996) to handle target at- tributes with multiple classes and missing data values. Their ... 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 ... ) AND a j ∈dom 1 (a j ) S    | S | +    σ a i ∈dom 2 (a i ) AND a j ∈dom 2 (a j ) S    | S | When the first split refers to attribute a i and it splits dom(a i ) into dom 1 (a i ) and

Ngày tải lên: 04/07/2014, 05:21

... 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 ... 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

Ngày tải lên: 04/07/2014, 05:21

... seven cases, and the frequencies n 3 jk . The full joint distribution is defined by the parameters θ 3 jk , and the parameters θ 1k and θ 2k that specify the marginal distributions of Y 1 and Y 2 . ... 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 ... with the number of candidate parents and successful heuristic search procedures (both deterministic and stochastic) have been proposed to render the task feasible (Cooper and Herskovitz, 1992,Larranaga

Ngày tải lên: 04/07/2014, 05:21

... in a compact and understand- able format. Data are expected to improve the understanding of institutions, busi- nesses, and citizens of the current state of affairs in the country and play a key ... micro-components and fail to convey an overall picture of the process underlying the data. A different approach to the analysis of survey data would be to employ Data Mining tools to generate hypothesis and ... (Sebastiani and Ramoni, 2000, Sebastiani and Ramoni, 2001B) to customer profiling (Sebastiani et al., 2000) and bioinformatics (Friedman, 2004,Sebastiani et al., 2004,2). Here we describe two Data Mining

Ngày tải lên: 04/07/2014, 05:21

... can be partially observed and data can be MCAR, MAR or IM. These two issues — learning mixed variables networks and handling incomplete databases — are still unsolved and they offer challenging ... Bayesian data analysis for Data Mining. In Handbook of Data Mining, pages 103–132. MIT Press, 2003. D. Madigan and J. York. Bayesian graphical models for discrete data. Int Stat Rev, pages 215–232, ... J Chen, and Z J Wang, editors, Genomic Signal Processing and Statistics, Series on Signal Processing and Communications. EURASIP, 2004. P. Sebastiani and M. Ramoni. Analysis of survey data with

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

... programs (e.g., LIBSVM: Chang and Lin 2001, SVMlight: Joachim 2004). Some solvers include integrated model selection and data rescaling proce- dures for improved speed and numerical stability. Hsu ... 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 ... to both clas- sification and regression problems. This chapter provides an overview of the main SVM methods for the separable and non-separable case and for classification and regression problems.

Ngày tải lên: 04/07/2014, 05:21

... disagreements. The Rand index is defined as: RAND = a + d 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 ... C 1 ; and d be the number of pairs of instances that are assigned to different clusters in C 1 and C 2 . The quantities a and d 278 Lior Rokach can be interpreted as agreements, and b and c as ... ) measure the similarity and distance of the vectors x j and 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

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

... 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 ... on RANdom Search) have been developed by Ng and Han (1994). This method identifies candidate cluster cen- troids by using repeated random samples of the original data. Because of the use of random ... Larsen, B. and Aone, C. 1999. Fast and effective text mining using linear-time document clustering. In Proceedings of the 5th ACM SIGKDD, 16-22, San Diego, CA. Maimon O., and Rokach, L. Data Mining

Ngày tải lên: 04/07/2014, 05:21

... dry fi ltration (such as in building air cleaning). 2C PAPER AND FABRICS Filters and Filtration Handbook Fifth Edition Ken Sutherland AMSTERDAM • BOSTON • HIEDELBERG • LONDON NEWYORK • OXFORD ... materials being handled. Wear products will be at their peak when the machine is new and is being bedded down, and at the same time residues from its manufacture (such as grains of foundry sand) could ... exhausts ● machinery and engine air intakes ● respirators and breathing apparatus ● compressed air treatment and pneumatic systems ● engine exhausts ● process and boiler furnace exhausts....

Ngày tải lên: 02/04/2014, 16:33

... 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 ... clustering, association rule mining, and attribute selection. Getting to know the data is is a very important part of Data Mining, and many data visualization facilities and data preprocessing tools are ... 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 ... enterprises. Thus, we have first hand experience in the needs of the KDD/DM community in research and practice. This handbook evolved from these experiences. The first edition of the handbook, which was published ... methodologies, trends, challenges and applica- tions of Data Mining into a coherent and unified repository. This handbook provides researchers, scholars, students and professionals with a comprehensive,...

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 ... Rokach 959 51 Data Mining using Decomposition Methods Lior Rokach, Oded Maimon 981 52 Information Fusion - Methods and Aggregation Operators Vicenc¸ Torra 999 53 Parallel And Grid-Based Data Mining ... Methods 34 Mining Multi-label Data Grigorios Tsoumakas, Ioannis Katakis, Ioannis Vlahavas 667 35 Privacy in Data Mining Vicenc¸ Torra 687 36 Meta-Learning - Concepts and Techniques Ricardo Vilalta,...

Ngày tải lên: 04/07/2014, 05:21

... 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. ... now avail- able to the researchers and practitioners. No one method is superior to others for all cases. The handbook of Data Mining and Knowledge Discovery from Data aims to organize all significant ... rationale, reasoning and organization of the handbook are presented in this chapter for helping the reader to navigate the extremely rich and detailed content provided in this handbook. In this chapter...

Ngày tải lên: 04/07/2014, 05:21