difference between data information and knowledge ppt

Advanced Information and Knowledge Processing ppt

... improve communication, understanding, and man-agement of medical knowledge and data. It is a multi-disciplinary scienceat the junction of medicine, mathematics, logic, and information technology,which ... ofInfection,andPathogens 45415.3.1 PatientExample(Part1) 45415.3.2 Fusion of Data and Knowledge for Calculation ofProbabilities for Sepsis and Pathogens 45615.4 CalculationofCoverage and TreatmentAdvice ... 46115.4.1 PatientExample(Part2) 46115.4.2 Fusion of Data and Knowledge for Calculation ofCoverageandTreatmentAdvice 46615.5 Calibration Databases 46715.6 ClinicalTesting ofDecision-supportSystems...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 2 pptx

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... Multimedia Data Mining58 Data Mining in MedicineNada Lavraˇc, Blaˇz Zupan 111159 Learning Information Patterns in Biological Databases - Stochastic Data MiningGautam B. Singh 113760 Data Mining ... Rokach 95951 Data Mining using Decomposition MethodsLior Rokach, Oded Maimon 98152 Information Fusion - Methods and Aggregation OperatorsVicenc¸ Torra 99953 Parallel And Grid-Based Data Mining ... 75940 Mining Concept-Drifting Data StreamsHaixun Wang, Philip S. Yu, Jiawei Han 78941 Mining High-Dimensional Data Wei Wang, Jiong Yang 80342 Text Mining and Information ExtractionMoty Ben-Dov,...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 3 pptx

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... 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, obtainingadditional necessary data, and then integrating all the data for the knowledge discovery ... theinteractive and iterative aspect of the KDD is taking place. It starts with thebest available data set and later expands and observes the effect in terms of knowledge discovery and modeling.3....

Data Mining and Knowledge Discovery Handbook, 2 Edition part 5 pptx

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... analyze, and investigate such very large data sets hasgiven rise to the ﬁelds of Data Mining (DM) and data warehousing (DW). Withoutclean and correct data the usefulness of Data Mining and data ... examiningdatabases, detecting missing and incorrect data, and correcting errors. Other recentwork relating to data cleansing includes (Bochicchio and Longo, 2003, Li and Fang,1989). Data Mining ... areasthat 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...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 6 ppt

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... 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/PurgeProblem, 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 — AHuman–Centered Approach. In Advances in Knowledge Discovery and Data Min-ing, Fayyad, U. M., Piatetsky-Shapiro,...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 10 ppt

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... (Silva and Tenenbaum, 2002). Landmark Isomap simply employs land-mark MDS (Silva and Tenenbaum, 2002) to addresses this problem, computing alldistances 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 undirectedgraph G by forming a one-to-one correspondence between nodes in the graph and data points. If two nodes i, j are connected...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 20 ppt

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... for Data Mining, Proc. 22nd Int. Conf. Very Large Databases, T. M. Vijayaraman and AlejandroP. Buchmann and C. Mohan and Nandlal L. Sarda (eds), 544-555, Morgan Kaufmann,1996.Sklansky, J. and ... TreeConstruction 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 DiscoveryHandbook, pp. 321–352, 2005, Springer.Rokach, L. and Maimon, O., Data mining for improving the...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 24 ppt

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... increases, the space that needs to be ﬁlledwith 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, machinelearning, reinforcement learning, algorithmic modeling and others. By Data Min-ing”...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 25 pptx

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... 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. Theupper left partition and the ... keydistinction between the more effective and the less effective Data Mining proceduresis how overfitting is handled. Finding new and improved ways to fit data is oftenquite easy. Finding ways to ... (e.g., 5).“Random forests” is one powerful approach exploiting these ideas. It builds onCART, and will generally fit the data better than standard regression models or CART13“Bagging” stands for...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 38 pptx

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... 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 353F. 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...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 61 pptx

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... wekeep the minimum distance between clusters, deﬁned by the distance between two points P1 and P2, such that P1belongs to the ﬁrst cluster and P2to the second, and P1 and P2are theclosest ... show experimental results of running time and cluster quality using arange 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 Issuesin Data Mining and Knowledge Discovery,...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 65 ppt

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... measure the degree of similarity between C and P:1. Rand Statistic: R =(a + d)/M2. 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 itcan be considered as a representative rule of our data set. Moreover! confidence expresses ourconfidence based on the available data ... association rules’ importance and confidence.They may represent the predictive advantage of a rule and help to identify interesting patternsof knowledge in data and make decisions. Below we shall...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 70 ppt

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

Data Mining and Knowledge Discovery Handbook, 2 Edition part 74 pptx

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... 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...

Data Mining and Knowledge Discovery Handbook, 2 Edition part 76 pptx

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... 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, ... PS(I(S)(x)|x) and P(y|x).Heskes (1998) adopts the natural variance term varC and, ignoring the residual er-ror, deﬁnes bias as the difference between the mean misclassiﬁcation error and hisvariance. ... b and minimizing squared error onthe learning set, we obtain the estimations given in the left part of Figure 39.1 for twoO. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook,...