hadoop mapreduce and dryad
Collective Intelligence in Action phần 1 pdf
... and Split Unregistered Version - http://www.simpopdf.com To my dear sons, Ayush and Shray, and my beautiful, loving, and intelligent wife, Alpana For online information and ordering of this and ... with Nutch 168 Apache Hadoop, MapReduce, and Dryad 169 ■ ■ ■ 6.4 6.5 PART Summary 171 Resources 171 DERIVING INTELLIGENCE 173 Data mining: process, toolkits, and standards 175 7.1 Core concepts ... Shiva Paranandi, for his help in reviewing the text and the code, and for his technical proofread; Brendan Murray, for his technical proofread of the first half of the book; Sean Handel, for...
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Collective Intelligence in Action phần 2 ppsx
... outlier and recompute the validation window for the sample set of [47, 50, 55, 47, 54, 45, 50] The new mean and standard deviation is 49.71 and 3.73 Forms of user interaction 39 Simpo PDF Merge and ... services and their advantages and disadvantages 2.1.1 Synchronous and asynchronous services For embedding intelligence in your application, you need to build two kinds of services: synchronous and ... services—event-driven and non–event-driven Let’s now look at the advantages and disadvantages of each of these approaches 2.1.4 Advantages and disadvantages of event-based and non–event-based architectures...
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Data warehuose and data mining
... biệt cho tất i, j Đó mối kết hợp khách hàng mua X, người có mua Y Hình thức LHS (left-hand side), RHS (righthand side) Thiết lập LHS ∪ RHS gọi tập hạng mục (itemset) Luật kết hợp ( Association ... Oriented Integrated Data Warehouse Non Volatile Time Variant Định nghĩa Kho Dữ Liệu (tt) • • Theo Pandora, Swinburn University : – Là phương thức cho việc kết nối liệu từ nhiều hệ thống khác – Là...
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Data Mining - Chapter 2
... [1] Jiawei Han, Micheline Kamber, “Data Mining: Concepts and Techniques”, Second Edition, Morgan Kaufmann Publishers, 2006 [2] David Hand, Heikki Mannila, Padhraic Smyth, “Principles of Data ... Springer-Verlag, 2008 [4] Graham J Williams, Simeon J Simoff, “Data Mining: Theory, Methodology, Techniques, and Applications”, Springer-Verlag, 2006 [5] ZhaoHui Tang, Jamie MacLennan, “Data Mining with ... (object matching) Vấn đề dư thừa (redundancy) Phát xử lý mâu thuẫn giá trị liệu (detection and resolution of data value conflicts) 2.1 Tổng quan giai đoạn tiền xử lý liệu Các kỹ thuật tiền...
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Data mining
... yêu cầu Partition, bạn đặt tên khác không vấn đề Partitions: Train and test : bạn chia mẫu làm hai thực kiểm tra Train,test and validation : thực hiện, kiểm tra xác nhận Training partition size ... Chọn để liệu mẫu cách vứt bỏ kỷ lục thứ n Ví dụ, n thiết lập đến 5,các hồ sơ lấy 5,10, 15, 20 • Random% Chọn mẫu ngẫu nhiên tỷ lệ phần trăm liệu Ví dụ, bạn thiết lập tỷ lệ phần trăm đến 20, 20% ... field names Chỉ định phương pháp xử lý tên biến nhãn xuất từ Clementine cho SPSS file SAV • Names and variable labels:Tên nhãn biến Tên xuất tên biến SPSS, nhãn xuất khNu nhãn biến SPSS • Names...
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Data Mining Tutorial
... size (# obsns.) and sum Prune (and split) to maximize profits Additional Ideas • Forests – Draw samples with replacement (bootstrap) and grow multiple trees • Random Forests – Randomly sample ... If the Life Line is long and deep, then this represents a long life full of vitality and health A short line, if strong and deep, also shows great vitality in your life and the ability to overcome ... Under H0 Conclusion: Estimated slopes vary Standard deviation (estimated) of sample slopes = “Standard error” Compute t = (estimate – hypothesized)/standard error p-value is probability of larger...
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data-mining-tutorial
... approach is to build BALANCED train and test sets, and train model on a balanced set randomly select desired number of minority class instances add equal number of randomly selected majority class ... heuristic also looks at real-time learning and robotics – areas not part of data mining Data Mining and Knowledge Discovery integrates theory and heuristics focus on the entire process ... potentially useful and ultimately understandable patterns in data from Advances in Knowledge Discovery and Data Mining, Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy, (Chapter 1), AAAI/MIT Press...
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hash-based approach to data mining
... hashing and pruning), PHP (perfect hashing and pruning) and PHS (Perfect hashing and data shrinking) [4,5,6,12] 11 Hash-Based Approach to Data Mining 2.1 DHP algorithm (direct hashing and pruning) ... In this chapter, I present three algorithms: Direct Hashing and Pruning (DHP), Perfect Hashing and Pruning (PHP) and Perfect Hashing and Shrinking (PHS) In these algorithms, the PHS is considered ... system : Candidate itemsets k elements : Database : Direct Hashing and Pruning : Hash table of k-itemsets : Large itemsets k elements : Perfect Hashing and DB Pruning : Perfect Hashing and data...
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Data mining and medical knowledge management cases and applications
... Information and Knowledge and communication technologies These new technologies are speeding an exchange and use of data, information and knowledge and are eliminating geographical and time barriers ... supported by arguments, and the explanation of decisions and predictions should be mandatory for an effective deployment of DM models DM and KM are thus becoming of great interest and importance for ... X, Y, and the following inequalities: ≤ I(X;Y) ≤ max {I(X) , I(Y)} (19) hold, with I(X;Y) = if and only if messages X and Y are independent, and: I(X;Y) = I(X) or I(X;Y) = I(Y) (20) if and...
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CUSTOMER SATISFACTION USING DATA MINING TECHNIQUES
... preference disaggregation methodology is an ordinal regression based approach (Lagrèze and Siskos, 1982; Siskos and Yannacopoulos, 1985) in the field of multicriteria analysis It is used for the ... satisfaction and his/her satisfaction with regard to the set of discrete criteria The collected data is analyzed with the preference disaggregation model, respecting the ordinal and qualitative ... form of customers’ judgements and preferences The main results of the method are (Grigoroudis et al., 1998; Siskos et al., 1998; Mihelis et al., 1998): • global and partial satisfaction functions,...
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Data Preparation for Data Mining- P3
... data and the data set, and various ways of structuring data in order to work with it Problems that afflict the data and the data set (and also the miner!) were introduced All of this data, and ... their limited data capacity and inability to handle certain types of operations needed in data preparation, data surveying, and data modeling For exploring small data sets, and for displaying various ... how and why it is transformed, and what can be done with and said about it Much more will be said about data as the techniques for manipulating it are introduced However, before examining how and...
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Data Preparation for Data Mining- P4
... to be used is identified and assembled and its basic structure and features are understood In this chapter we look at the process of finding and assembling the data and assessing the basic characteristics ... forms: super, macro, and micro Superstructure refers to the scaffolding erected to capture the data and form a data set The superstructure is consciously and deliberately created and is easy to see ... sit between the real-world data, cleaning and preparing the incoming data stream identically with the way the training and test sets were prepared, and converting predicted, transformed values...
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Data Preparation for Data Mining- P5
... watermark other data modeling methodologies now available Just understand the data, get whatever insight is possible, and understand the purpose for collecting it 4.2.5 Relationship With multiple ... point-of-sale (POS) transactions and wanted to discover the main factors driving catalog orders The POS transactions recorded date and time, department, dollar amount, and tender type in addition ... confidence level that the variability was captured (Confidence levels, and how they are discovered and used, are covered in Chapter and are not used in the assay.) • REQ The minimum number of records...
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Data Preparation for Data Mining- P6
... mean and has the least standard deviation The bimodal is more bunched than the linear, and less than the normal, and its standard deviation indicates this, as expected Standard deviation is a way ... values set at the mean-plus-1 standard deviation and the mean-minus-1 standard deviation This is normally expressed as m ± s, where m is the mean and s the standard deviation It is also known, ... preparation 5.3.2 Variability and Convergence Differently sized, randomly selected samples from the same population will have different variability measures As a larger and larger random sample is taken,...
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Oracle 10g Data Mining Administrators Guide WW
... the lab and then imported to the scoring engine Model export and import can be a routine procedure As new data are accumulated, data mining specialists will build and test more new models, and newer ... discussion in PL/SQL Packages and Types Reference Oracle strongly recommends that you use the new Data Pump Export and Import utilities (expdp and impdp) for database export and import starting with ... with ODM Model Export and Import Data mining model export and import jobs utilize and manage two temporary tables in the data mining user schema: DM$P_MODEL_EXPIMP_ TEMP and DM$P_MODEL_TABKEY_TEMP...
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Oracle9i Data Mining Concepts Release 9.2.0.2 October 2002 Part No. A95961-02 Oracle9i Data
... model and then using that model to score new data Appendix A: Lists ODM sample programs and outlines how to compile and execute them Glossary: A glossary of terms related to data mining and ODM ... dataset with two attributes: AGE and HEIGHT, the following rule represents most of the data assigned to cluster 10: If AGE >= 25 and AGE = 5.0ft and HEIGHT
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Data Preparation for Data Mining- P7
... carefully and should report the location and extent of any such areas in the data At least when modeling in such an area of the data, the miner can place a large sign “Warning—Quicksand!” on the ... space A modeling tool has to search and characterize state space, and too many dimensions means that the data points disappear into a thin mist! 6.2.4 Neighbors and Associates Please purchase PDF ... particular association between age and height, except that this range has lower and upper limits In the age dimension, the lower limit is the age at which growth stops, and the upper limit is the age...
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Data Preparation for Data Mining- P8
... normalized values are being used, both “H” and “T” should have about the same value, and “M” and “A” should have about the same value, as should “L” and “S.” This does not suggest that Please ... understand what problems the out-of-range values cause in each stage 7.1.1 Review of Data Preparation and Modeling (Training, Testing, and Execution) Chapter described the creation, use, and purpose ... out-of-range values and one for the lower out-of-range values, need to be squashed and attached to the linear part of the transform Taking the linear part of the range and adding the upper and lower transforms...
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Data Preparation for Data Mining- P9
... These two box and whisker plots show the before and after redistribution positions—normalized only (a) and normalized and redistributed (b)—for standard deviation, standard error, and mean values ... the quartile range, and the median, to values that better balance the distribution Figures 7.11(a) and 7.11(b) show similar figures for standard deviation, standard error, and mean These measures ... Figure 7.10 These two box and whisker plots show the before and after redistribution positions—normalized only (a) and normalized and redistributed (b)—for maximum, minimum, and median values Comparing...
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Principles of data mining
... on the other hand, is a dissimilarity measure that satisfies three conditions: d(i, j) = for all i and j, and d(i, j) = if and only if i = j; d(i, j) = d(j, i) for all i and j; and d(i, j) = ... networks, and databases and have also developed new methods targeted at large data mining problems Principles of Data Mining by David Hand, Heikki Mannila, and Padhraic Smyth provides practioners and ... daily since 1948 (see Cheng and Wallace [1993] and Smyth, Idea, and Ghil [1999] for further discussion) Predictive Modeling: Classification and Regression (chapters 10 and 11): The aim here is to...
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