advanced algorithms for data mining

... the data set for mining to best expose the information contained in it to the mining tool Indeed, the whole purpose for mining data is to transform the information content of a data set that ... transforming information The concept of information is crucial to data mining It is the very substance enfolded within a data set for which the data set is being mined It is the reason to prepare the data ... Transformations and Difficulties—Variables, Data, and Information Much of this discussion has pivoted on information—information in a data set, information content of various scales, and transforming...

Ngày tải lên: 24/10/2013, 19:15

... bias Determining data structure Building the PIE Surveying the data Modeling the data 3.3.1 Stage 1: Accessing the Data The starting point for any data preparation project is to locate the data This ... execution data is in its “raw” form, and the model works only with prepared data, it is necessary to transform the execution data in the same way that the training and test data were transformed ... preparation activities Data Issue: Representative Samples A perennial problem is determining how much data is needed for modeling One tenet of data mining is “all of the data, all of the time.”...

Ngày tải lên: 24/10/2013, 19:15

... original information This additional information actually forms another data stream and enriches the original data Enrichment is the process of adding external data to the data set Note that data enhancement ... example of enhancing the data No external data is added, but the existing data is restructured to be more useful in a particular situation Another form of data enhancement is data multiplication When ... between variables also needs to be considered In every data mining application, the data set used for mining should have some underlying rationale for its use Each of the variables used should have...

Ngày tải lên: 29/10/2013, 02:15

... numerating the alphas, but also for conducting the data survey and for addressing various problems and issues in data mining Becoming comfortable with the concept of data existing in state space ... standard deviation of the sample For large numbers of instances, which will usually be dealt with in data mining, the difference is miniscule.) There is another formula for finding the value of the ... of the original data sample Random sampling does that If the original data set represents a biased sample, that is evaluated partly in the data assay (Chapter 4), again when the data set itself...

Ngày tải lên: 29/10/2013, 02:15

... 0.8769 Forward 0.4940 0.4923 Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark Forward 0.6988 0.7692 Forward 0.4940 0.4462 Forward 0.6988 0.7538 Forward 0.4940 0.3231 Forward ... Zalapski Forward 37 Patrick Poulin Reserve 55 Igor Ulanov Forward 26 Martin Rucinsky Defense 43 Patrice Brisebois Forward 28 Marc Bureau Forward 27 Shayne Corson Defense 52 Craig Rivet Forward ... distance from there to each of the nearest data points in each dimension The mean distance to neighboring data points serves as a surrogate measurement for density For many purposes this is a more convenient...

Ngày tải lên: 08/11/2013, 02:15

... Translating the information discovered there into insights about the data, and the objects the data represents, forms an important part of the data survey in addition to its use in data preparation ... with putting data into the multitable structures called “normal form” in a database, data warehouse, or other data repository.) During the process of manipulation, as well as exposing information, ... a working data preparation computer program were also addressed In spite of the distance covered here, there remains much to to the data before it is fully prepared for surveying and mining Please...

Ngày tải lên: 08/11/2013, 02:15

... least harm to the information content of the data set Yet it still leaves some information exposed for the mining tools to use when values outside those within the sample data set are encountered ... are somehow regularized For instance, one such tool for a particular data set could, when fine-tuned and adjusted, just as well with unprepared data as with prepared data The difference was that ... work.) Third, and very important for maximum information exposure, the individual variable distributions are transformed This transformation makes the between-variable information far more accessible...

Ngày tải lên: 08/11/2013, 02:15

... Series Data Series data differs from the forms of data so far discussed mainly in the way in which the data enfolds the information The main difference is that the ordering of the data carries information ... series data set so that it can be accurately and completely characterized Find methods for manipulating the unique features of series data to expose the information content to mining tools Series data ... technique Figure 9.11 Waterforms and their correlograms 9.4 Modeling Series Data Given these tools for describing series data, how they help with preparing the data for modeling? There are two...

Ngày tải lên: 15/12/2013, 13:15

... transform accomplishes this The second transform subtracts the mean of the transformed variable from each transformed value, and divides the result by the standard deviation The formula for this ... transform accomplishes this The second transform subtracts the mean of the transformed variable from each transformed value, and divides the result by the standard deviation The formula for this ... uniform spectrum and uniformly low autocorrelation at all lags There still might be useful information contained in the waveform, but the chance is small This is a good sign that extra effort...

Ngày tải lên: 15/12/2013, 13:15

... than data preparation? Data preparation concentrates on transforming and adjusting variables’ values to ensure maximum information exposure Data surveying concentrates on examining a prepared data ... large for the mining tool the customer had selected, causing repeated mining software failures and system crashes during mining The data reduction methodology described above reduced the data ... nomenclature A function can be expressed as a formula, just as the formula for determining the value of the logistic function is For convenience, this whole formula can be taken as a given and represented...

Ngày tải lên: 15/12/2013, 13:15

... “information.” This book mentions “information” in several places “Information is embedded in a data set.” “The purpose of data preparation is to best expose information to a mining tool.” “Information ... that mining is not designed to extract information Data, or the data set, enfolds information This information describes many and various relationships that exist enfolded in the data When mining, ... term “information” is used in data mining Data possesses information only in its latent form Mining provides the mechanism by which any insight potentially present is explicated Since information...

Ngày tải lên: 15/12/2013, 13:15

... determining the confidence that the multivariable variability of a data set is captured, entropic analysis forms the main tool for surveying data The other tools are useful, but used largely for ... full range of calculations for forward and reverse entropy, signal entropy and mutual information, even for this simplified example, are quite extensive For instance, determining the entropy of each ... of the instances can be assembled into a data set, and that data set examined for similarity to the training data set, but that only tells you that the data set now assembled was or wasn’t drawn...

Ngày tải lên: 15/12/2013, 13:15

... reason for using the best data preparation available—is that data preparation adds value to the business objective that the miner is addressing 12.1 Modeling Data Before examining the effect the data ... map for the CREDIT data set in Figure 11.31 that carries useful information In spite of the apparent perfect predictions possible from the information enfolded in this data (shown in the information ... 11.32 Information metrics for the unbalanced CREDIT data set on the left, and the balanced CREDIT data set on the right The unbalanced data set has less than 1% buyers, while the balanced data set...

Ngày tải lên: 15/12/2013, 13:15

... unprepared data shows an 81.8182% accuracy on the test data set (top) and an 85.8283% accuracy in the test data for the prepared data set (bottom) 12.4 Practical Use of Data Preparation and Prepared Data ... which data mining tools and data modeling tools focus The near future will see the development of automated data preparation tools for series data Approaches for automated series data preparation ... model is needed, data extracts for training, test, and evaluation data sets can be prepared and models built on those data sets For any continuously operating model, the Prepared Information Environment...

Ngày tải lên: 15/12/2013, 13:15

... unprepared data shows an 81.8182% accuracy on the test data set (top) and an 85.8283% accuracy in the test data for the prepared data set (bottom) 12.4 Practical Use of Data Preparation and Prepared Data ... which data mining tools and data modeling tools focus The near future will see the development of automated data preparation tools for series data Approaches for automated series data preparation ... the data in a very different way A tree can digest unprepared data, and also is not as sensitive to balancing of the data set as a network Does data preparation help improve performance for a...

Ngày tải lên: 15/12/2013, 13:15

... http://www.cs.uvm.edu/∼icdm/) identified the top 10 algorithms in data mining for presentation at ICDM ’06 in Hong Kong This book presents these top 10 data mining algorithms: C4.5, k-Means, SVM, Apriori, ... LLC 1.6 Advanced Topics 1.6 13 Advanced Topics With the massive data emphasis of modern data mining, many interesting research issues in mining tree/rule-based classifiers have come to the forefront ... on Knowledge Discovery and Data Mining) , ICDM ’06 (the 2006 IEEE International Conference on Data Mining) , and SDM ’06 (the 2006 SIAM International Conference on Data Mining) , as well as the ACM...

Ngày tải lên: 17/02/2014, 01:20

... different values for the attribute A, which we denote Ua = σA=a (R(A) S(A, B)) for each a ∈ A Our goal is to form the intersection Ua ∩ T (A) for each such a This procedure performs the same intersection ... index the data While these data structures are ubiquitous in modern database systems, from a theoretical perspective they may not be optimal for join pro- R(A) S(A, B) T (B) This argument for two ... develop join algorithms that are instance optimal (up to polylog factors) In particular, we present such an algorithm for acyclic queries assuming data is stored in Binary Search Trees (henceforth BSTs),...

Ngày tải lên: 30/03/2014, 22:20

... to perform more passes over the data – May miss some potentially interesting cross© Tan,Steinbach, Kumar Introduction to Data Mining 25 level association patterns Sequence Data Sequence Database: ... (maximum span) Frequent Subgraph Mining Extend association rule mining to finding frequent subgraphs Useful for Web Mining, computational chemistry, bioinformatics, spatial data sets, etc Homepage Research ... association rule mining algorithms Determine interesting rules in the output © Tan,Steinbach, Kumar Introduction to Data Mining Approach by Srikant & Agrawal Discretization will lose information Approximated...

Ngày tải lên: 15/03/2014, 09:20

... Introduction to Data Mining 17 Experimental Results: CHAMELEON © Tan,Steinbach, Kumar Introduction to Data Mining 18 Experimental Results: CHAMELEON © Tan,Steinbach, Kumar Introduction to Data Mining ... to Data Mining 20 Experimental Results: CURE (15 clusters) © Tan,Steinbach, Kumar Introduction to Data Mining 21 Experimental Results: CHAMELEON © Tan,Steinbach, Kumar Introduction to Data Mining ... graph partitioning algorithms (or algorithms based on graph partitioning algorithms – Chameleon and Hypergraph-based Clustering © Tan,Steinbach, Kumar Introduction to Data Mining 10 Sparsification...

Ngày tải lên: 15/03/2014, 09:20

... Introduction to Data Mining 46 Rule Generation for Apriori Algorithm Lattice of rules Low Confidence Rule Pruned Rules © Tan,Steinbach, Kumar Introduction to Data Mining 47 Rule Generation for Apriori ... Introduction to Data Mining 32 Alternative Methods for Frequent Itemset Generation Traversal of Itemset Lattice – Equivalent Classes © Tan,Steinbach, Kumar Introduction to Data Mining 33 Alternative ... Representation of Database – horizontal vs vertical data layout © Tan,Steinbach, Kumar Introduction to Data Mining 35 FP-growth Algorithm Use a compressed representation of the database using an...

Ngày tải lên: 15/03/2014, 09:20