conceptual difference between data information and knowledge ppt

... improve communication, understanding, and man- agement of medical knowledge and data. It is a multi-disciplinary science at the junction of medicine, mathematics, logic, and information technology, which ... of Infection,andPathogens 454 15.3.1 PatientExample(Part1) 454 15.3.2 Fusion of Data and Knowledge for Calculation of Probabilities for Sepsis and Pathogens 456 15.4 CalculationofCoverage and TreatmentAdvice ... 461 15.4.1 PatientExample(Part2) 461 15.4.2 Fusion of Data and Knowledge for Calculation of CoverageandTreatmentAdvice 466 15.5 Calibration Databases 467 15.6 ClinicalTesting ofDecision-supportSystems...

Ngày tải lên: 29/03/2014, 11:20

... 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 ... 759 40 Mining Concept-Drifting Data Streams Haixun Wang, Philip S. Yu, Jiawei Han 789 41 Mining High-Dimensional Data Wei Wang, Jiong Yang 803 42 Text Mining and Information Extraction Moty Ben-Dov,...

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. ... Process of Knowledge Discovery in Databases. be determined. This includes finding out what data is available, obtaining additional necessary data, and then integrating all the data for the knowledge discovery ... the interactive and iterative aspect of the KDD is taking place. It starts with the best available data set and later expands and observes the effect in terms of knowledge discovery and modeling. 3....

Ngày tải lên: 04/07/2014, 05:21

... analyze, and 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 ... examining databases, detecting missing and incorrect data, and correcting errors. Other recent work relating to data cleansing includes (Bochicchio and Longo, 2003, Li and Fang, 1989). Data Mining ... 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 Quality Management...

Ngày tải lên: 04/07/2014, 05:21

... Data Warehousing and Knowledge Discovery; 2002 September 04-06; 170-180. Hernandez, M. & Stolfo, S. Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem, Data Mining and Knowledge ... Methods, Data Mining and Knowledge Discov- ery Handbook, Springer, pp. 321-352. Simoudis, E., Livezey, B., & Kerber, R., Using Recon for Data Cleaning. In Advances in Knowledge Discovery and Data ... France. 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,...

Ngày tải lên: 04/07/2014, 05:21

... (Silva and Tenenbaum, 2002). Landmark Isomap simply employs land- mark MDS (Silva and Tenenbaum, 2002) to addresses this problem, computing all distances as geodesic distances to the landmarks. ... clustering and Laplacian eigen- maps are local (for example, LLE attempts to preserve local translations, rotations and scalings of the data) . Landmark Isomap is still global in this sense, but the land- mark ... Let’s start by defining a simple mapping from a dataset to an undirected graph G by forming a one-to-one correspondence between nodes in the graph and data points. If two nodes i, j are connected...

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 ... 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 ... 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...

Ngày tải lên: 04/07/2014, 05:21

... 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 ... almost as many definitions of Data Mining as there are treatises on the sub- ject (Sutton and Barto, 1999, Cristianini and Shawe-Taylor, 2000, Witten and Frank, 2000,Hand et al., 2001,Hastie et ... 2001b,Dasu and Johnson, 2003), and associated with Data Mining are a variety of names: statistical learning, machine learning, reinforcement learning, algorithmic modeling and others. By Data Min- ing”...

Ngày tải lên: 04/07/2014, 05:21

... partition. 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 ... key 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 ... (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...

Ngày tải lên: 04/07/2014, 05:21

... type of data is often referred to as relational data. This is as opposed to attribute vector data used by many other unsupervised and supervised Data Mining techniques. In most standard Data Mining ... (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.1007/978-0-387-09823-4_18, © Springer Science+Business Media, LLC 2010 17 Constraint-based Data Mining 353 F. Bonchi and C. ... sets and condensed representations. In Proc. KDD’96, pages 189–194. AAAI Press, 1996. H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discov- ery. Data Mining and...

Ngày tải lên: 04/07/2014, 05:21

... we keep the minimum distance between clusters, defined by the distance between two points P 1 and P 2 , such that P 1 belongs to the first cluster and P 2 to the second, and P 1 and P 2 are the closest ... show experimental results of running time and cluster quality using a range of data sets of increasing sizes and a high-dimensional data set. First, we use data sets whose distribution follows the ... Bradley, U. Fayyad, and C. Reina. Scaling Clustering Algorithms to Large Databases (Extended Abstract). In Proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery,...

Ngày tải lên: 04/07/2014, 05:21

... measure the degree of similarity between C and P: 1. Rand Statistic: R =(a + d)/M 2. Jaccard Coefficient: J = a/(a+ b + c) The above two indices range between 0 and 1, and are maximized when m=s. Another ... left hand side and right hand side of this rule and thus it can be considered as a representative rule of our data set. Moreover! confidence expresses our confidence based on the available data ... association rules’ importance and confidence. They may represent the predictive advantage of a rule and help to identify interesting patterns of knowledge in data and make decisions. Below we shall...

Ngày tải lên: 04/07/2014, 05:21

... an evaluation data set. A different line of attacking the multi-label feature selection problem is to transform the multi-label data set into one or more single-label data sets and use existing ... for single-label data, such as linear discriminant analysis (LDA), require modification prior to their application to multi-label data. LDA has been modified to handle multi-label data in (Park & ... each equal to the difference between the initial instance and one of the prototype vectors. A two level classification strategy is then employed to learn form the transformed data set. 34.2.2 Algorithm...

Ngày tải lên: 04/07/2014, 05:21

... Identities in Microdata Release, IEEE Trans. on Knowledge and Data Engineering, 13:6 1010-1027. Samarati, P., Sweeney, L. (1998) Protecting privacy when disclosing information: k- anonymity and its enforcement ... K., Kargupta, H., Ryan, J. (2006) Random projection based multiplicative data pertur- bation for privacy preserving data mining, IEEE Trans. on Knowledge and Data Engi- neering 18:1 92-106. Machanavajjhala, ... (2005) Probabilistic information loss mea- sures in confidentiality protection of continuous microdata, Data Mining and Knowledge Discovery, 11:2 181-193. Moore, R. (1996) Controlled data swapping techniques...

Ngày tải lên: 04/07/2014, 05:21

... Metrics for Evaluation of Data- mining Algorithms. In Proceedings of the Third International Conference on Knowledge Discovery and Data- Mining, 1997. Paterson, I. New Models for Data Envelopment Analysis, ... P S (I(S)(x) | x) and P(y | x). Heskes (1998) adopts the natural variance term var C and, ignoring the residual er- ror, defines bias as the difference between the mean misclassification error and his variance. ... b and minimizing squared error on the learning set, we obtain the estimations given in the left part of Figure 39.1 for two O. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook,...

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

Ngày tải lên: 04/07/2014, 05:21