a data mining knowledge discovery process model

Ngày tải lên: 28/06/2014, 10:20

... systems Today, data mining is both a technology that blends data analysis methods with sophisticated algorithms for processing large data sets, and an active research field that aims at developing new data ... Miller and J. Han. Geographic data mining and knowledge discovery: An overview. In Geographic Data Mining and Knowledge Discovery, pp. 3–32. Taylor and Francis, 2001. 13. A. Moore, P. Whigwham, A. ... Data Mining Mobility data mining is, therefore, emerging as a novel area of research, aimed at the analysis of mobility data by means of appropriate patterns and models extracted by efficient algorithms;...

Ngày tải lên: 25/03/2014, 11:52

... 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 transformation, ... Digital Assistant. The main disadvantage is that most of the functionality is only applicable if all data is held in main memory. A few algorithms are included that are able to process data incrementally ... graphically through visualization of the data and examination of the model (if the model structure is amenable to visualization). Users can also load and save models. Eibe Frank et al. 66 Weka -A Machine...

Ngày tải lên: 04/07/2014, 05:21

... edition. Ad- vances occurred in areas, such as Multimedia Data Mining, Data Stream Mining, Spatio-temporal Data Mining, Sequences Analysis, Swarm Intelligence, Multi-label classification and privacy ... in Data Mining, such as statistical methods for Data Mining, logics for Data Mining, DM query languages, text mining, web mining, causal discovery, ensemble methods, and a great deal more. Part ... identifying valid, novel, useful, and understandable patterns from large datasets. Data Mining (DM) is the mathematical core of the KDD process, involving the inferring algorithms that explore the data, ...

Ngày tải lên: 04/07/2014, 05:21

... Time Series Data Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn Keogh, Michail Vlachos, Gautam Das 1049 Part VII Applications 57 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 for Financial Applications Boris Kovalerchuk, ... Intelligence Approach Swagatam Das, Ajith Abraham 469 Contents XIII 54 Collaborative Data Mining Steve Moyle 1029 55 Organizational Data Mining Hamid R. Nemati, Christopher D. Barko 1041 56 Mining...

Ngày tải lên: 04/07/2014, 05:21

... unknown patterns. The model is used for understanding phenomena from the data, analysis and prediction. The accessibility and abundance of data today makes Knowledge Discovery and Data Mining a matter ... Knowledge Discovery and Data Mining 3 Fig. 1.1. The Process of Knowledge Discovery in Databases. be determined. This includes finding out what data is available, obtaining additional necessary data, and ... dynamic. Data structures may change (certain attributes become unavailable), and the data domain may be modified (such as, an attribute may have a value that was not assumed before). 1.2 Taxonomy...

Ngày tải lên: 04/07/2014, 05:21

... Multimedia Data Mining (Chapter 57). Multimedia data mining, as the name suggests, presumably is a combination of the two emerging areas: mul- timedia and data mining. Instead, the multimedia data mining ... 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 owing nature. Mohamed Medhat Gaber, Arkady Zaslavsky, and ... analyze only flat tables, in recent years new mature techniques have been developed for mining rich data formats: • Data Stream Mining - The conventional focus of data mining research was on mining resident...

Ngày tải lên: 04/07/2014, 05:21

... The major areas that include data cleansing as part of their defining processes are: data warehousing, knowledge discovery in databases, and data/ information quality management (e.g., Total Data ... investigate such very large data sets has given rise to the fields of Data Mining (DM) and data warehousing (DW). Without clean and correct data the usefulness of Data Mining and data warehousing ... mit- igated. Thus, data cleansing is a necessary precondition for successful knowledge discovery in databases (KDD). 2.2 DATA CLEANSING BACKGROUND There are many issues in data cleansing that researchers...

Ngày tải lên: 04/07/2014, 05:21

... 464-467. Brachman, R. J., Anand, T., The Process of Knowledge Discovery in Databases — A Human–Centered Approach. In Advances in Knowledge Discovery and Data Min- ing, Fayyad, U. M., Piatetsky-Shapiro, ... Information Patterns and Data Cleaning. In Advances in Knowledge Discovery and Data Mining, Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurasamy, R., eds. MIT Press/AAAI Press, 1996. Hamming, ... Very Large Data Bases; 1998 New York. 392-403. 32 Jonathan I. Maletic and Andrian Marcus Wang, R., Storey, V., & Firth, C. A Framework for Analysis of Data Quality Research, IEEE Transactions...

Ngày tải lên: 04/07/2014, 05:21

... identified as a chase algorithm, was also discussed in (Dardzinska and Ras, 200 3A, Dardzinska and Ras, 2003B). Learning missing attribute values from summary constraints was reported in (Wu and Barbara, ... is a Monte Carlo method of handling missing attribute values in which missing attribute values are replaced by many plausible values, then many complete data sets are analyzed and the results are ... Directions in Data Mining, as- sociated with the third IEEE International Conference on Data Mining, Melbourne, FL, November 1922, 24–30, 200 3A. Dardzinska A. and Ras Z.W. On rule discovery from...

Ngày tải lên: 04/07/2014, 05:21

... Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo CA (1993). Schafer J.L. Analysis of Incomplete Multivariate Data. Chapman and Hall, London, 1997. Slowinski R. and Vanderpooten ... variance of the projection of the data along n is just λ 1 . The above construction captures the variance of the data along the direction n. To characterize the remaining variance of the data, ... feature extractor would simply map the data to its class labels, for the classification task. On the other hand, a character recog- nition neural net can take minimally preprocessed pixel values...

Ngày tải lên: 04/07/2014, 05:21

... for audio or video data) and to make the features more robust. The above features, computed by taking projections along the n’s, are first translated and normalized so that the signal data has ... at once. The same is true for the directions that maximize the variance. Again, note that this argument holds however your data is distributed. PCA Maximizes Mutual Information on Gaussian Data Now ... points in the dataset (note that this measure can be very general, and in particular can allow for non- vectorial data) . Given this, MDS searches for a mapping of the (possibly further transformed)...

Ngày tải lên: 04/07/2014, 05:21

... the quality of the approximation can be characterized by the unex- plained variance, we can characterize the quality of the approximation here by the squared residuals. Let ¯ A have rank r, and ... Linear Embedding Locally linear embedding (LLE) (Roweis and Saul, 2000) models the manifold by treating it as a union of linear patches, in analogy to using coordinate charts to pa- rameterize a ... dimensional manifold embedded in a 50 dimensional space. The basic idea is to construct a graph whose nodes are the data points, where a pair of nodes are adjacent only if the two points are close...

Ngày tải lên: 04/07/2014, 05:21

... Reduction and Feature Selection Barak Chizi 1 and Oded Maimon 1 Tel-Aviv University Summary. Data Mining algorithms search for meaningful patterns in raw data sets. The Data Mining process requires ... using cross- validation (a wrapper approach) to estimate the accuracy of tables (and hence feature sets). The MDL approach was shown to be more efficient than, and perform as well as, as cross- validation. An ... Chizi and Oded Maimon Fig. 5.1. typical cost-error relation in a classification models. classifier that uses only a part of the data h ≤h ∗ and produces an increased error rate. In practice, the exact...

Ngày tải lên: 04/07/2014, 05:21

... 97 5.3.4 Factor Analysis (FA) Like PCA, factor analysis (FA) is also a linear method, based on the second-order data summaries. First suggested by psychologists, FA assumes that the measured variables ... that wrappers that employ instance based learners (includ- ing RC) are unsuitable for use on databases containing many instances because they are quadratic in N (the number of instances). Kohavi ... wrapper feature selection. Furthermore, when few examples are avail- able, or the data is noisy, standard wrapper approaches can detect globally irrelevant features more easily than RC. Domingos also...

Ngày tải lên: 04/07/2014, 05:21

... value range of the quantitative data. It then as- sociates a qualitative value to each interval. A cut point is a value among the quanti- tative data where an interval boundary is located by a ... referred to as categorical data, are data that can be placed into distinct categories. Qualitative data sometimes can be arrayed in a mean- ingful order. But no arithmetic operations can be applied ... Time-insensitive discretization only uses the stationary pro-perties of the quantitative data. 9. Ordinal vs. Nominal. Ordinal discretization transforms quantitative data into ordinal qualitative data. It aims at taking...

Ngày tải lên: 04/07/2014, 05:21

Ngày tải lên: 04/07/2014, 05:21

Ngày tải lên: 04/07/2014, 05:21

Ngày tải lên: 04/07/2014, 05:21

Ngày tải lên: 04/07/2014, 05:21