data mining and knowledge management ppt

... turnstile data stream model where insertion and deletion from the data are allowed. The algorithm dynamically works with any range of data and does not need any prior knowledge about the data. The ... mining and querying sys- tem. The system can classify, cluster, count frequency and query over data streams. Mining Alarming Incidents of Data Streams MAIDS is currently under develop- ment and ... functionalities are portfolio management using a mobile micro-database to store portfolio data and information about user’s preferences, and construction of the WatchList and this is the first point

Ngày tải lên: 04/07/2014, 05:21

... Philip S. Yu, and Jiawei Han Incremental or online Data Mining methods (Utgoff, 1989, Gehrke et al., 1999) are another option for mining data streams. These methods continuously revise and refine ... overfitting and the problems of conflicting concepts, the expiration of old data must rely on data? ??s distribution instead of only their arrival time. The ensemble ap- 40 Mining Concept-Drifting Data ... dimensional data is partitioned into sequential chunks based on their arrival time. Let S i be the data that came in between time t i and t i+1 . Figure 40.1 shows the distri- bution of the data and

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... J., Kamber, M., and Tung, A. (2001). Spatial Clustering Methods in Data Mining: A Survey. In Miller, H. and Han, J., editors, Geographic Data Mining and Knowledge Discovery. Taylor and Francis. ... on the features of spatial data mining that distinguish it from classical Data Mining. We have discussed major research accomplishments and techniques in spatial Data Mining, especially those related ... Estivill-Castro, V. and Murray, A. (1998). Discovering Associations in Spatial Data - An Efficient Medoid Based Approach. In Proc. of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining.

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... Advances in Knowledge Discovery and Data Mining. MIT Press, 1996. Flach, P.A., et al., Decision support for Data Mining: introduction to ROC analysis and its application. In Data Mining and Decision ... attempt to standardise the process of Data Mining. In CRISP-DM, six interrelated phases are used to describe the Data Mining process: business understanding, data understanding, data prepa- ration, ... requirements and estimation of risk is performed. In the data understanding phase data collected and char- acterized. Data quality is also assessed. During data preparation, tables, records and attributes

Ngày tải lên: 04/07/2014, 06:20

... knowledge and data stored in medical databases require the development of specialized tools for accessing the data, data analysis, knowledge discovery, and effective use of stored knowledge and ... amounts of data stored in medical databases require the development of specialized tools for accessing the data, data analysis, knowledge discovery, and effective use of stored knowledge and data. ... patient management purposes. The emerg- ing standards that relate to Data Mining are CRISP-DM and PMML. CRISP-DM is a Data Mining process standard that was crafted by Cross-Industry Standard Process

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... possible to perform data mining on the results. Data mining will result in validated results and further knowledge discovery. This part of NHECD results is targeted at Nanotox scientists and regulators. ... Health and Environmental Commented Database 1239 64.5 Further research Graph and table mining NHECD makes resort to text mining algorithms, allowing for information extraction from textual data. ... for documents (e.g., unstructured data) , metadata (semi structured to structured data, mostly in XML) and extracted information (mostly struc- tured, tabular data) . NHECD assumes that all target

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... relationship management (CRM), 1043, 1181, 1189 Data cleaning, 19, 615 Data collection, 1084 Data envelop analysis (DEA), 968 Data management, 559 Data mining, 10 82 Data Mining Tools, ... clustering, association rule mining, and attribute selection. Getting to know the data is is a very important part of Data Mining, and many data visualization facilities and data preprocessing tools ... used in all forms of Data Mining applications—from bioin- formatics to competition datasets issued by major conferences such as Knowledge Discovery in Databases. New Zealand has several research

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... Data Mining and Knowledge Discovery, 15(1):87-97, 2007. Larose, D.T., Discovering knowledge in data: an introduction to data mining, John Wiley and Sons, 2005. Maimon O., and Rokach, L. Data Mining ... tools and techniques, Morgan Kaufmann Pub, 2005. Wu, X. and Kumar, V. and Ross Quinlan, J. and Ghosh, J. and Yang, Q. and Motoda, H. and McLachlan, G.J. and Ng, A. and Liu, B. and Yu, P.S. and ... Knowledge Discovery and Data Mining 15 Rokach, L., Maimon, O., Data Mining with Decision Trees: Theory and Applications, World Scientific Publishing, 2008. Witten, I.H. and Frank, E., Data Mining: Practical

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... Latkowski and Mikolajczyk, 2004). In this method a data set is decomposed into complete data subsets, rule sets are induced from such data subsets, and finally these rule sets are merged. 3 Handling ... Foundations and New Directions in Data Mining, as- sociated with the third IEEE International Conference on Data Mining, Melbourne, FL, November 1922, 24–30, 2003A. Dardzinska A. and Ras Z.W. ... Foundations and New Directions in Data Mining, asso- ciated with the third IEEE International Conference on Data Mining, Melbourne, FL, November 1922, 31–35, 2003B. Greco S., Matarazzo B., and Slowinski

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... right hand side where d m and d > r, and ap- proximate the eigenvector of the full kernel matrix K mm by evaluating the left hand rows (and hence columns) are linearly independent, and suppose ... 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 zero mean and ... of the x’s and that of the y’s is max- imized (Baldi and Hornik, 1995, Diamantaras and Kung, 1996). Since the mapping W is deterministic, the conditional entropy H(y|x) vanishes, and the mutual

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... Dimension 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 ... minimal error rate ε ∗ and costs h ∗ to be derived). On some occasions, one might prefer using an inferior O. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI ... ‘modest’ size of 10 attributes. Data- mining algorithms are computationally intensive. Figure 5.1 describes the typical trade-off between the error rate of a Data Mining model and the cost of ob- taining

Ngày tải lên: 04/07/2014, 05:21

... ligent Data Analysis, Volume 9, Number 2, 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 ... quantitative data into qualitative data. Data Mining applications often involve quantitative data. However, there exist many learning algorithms that are primarily oriented to handle qualitative data ... Summary. Data- mining applications often involve quantitative data. However, learning from quantitative data is often less effective and less efficient than learning from qualitative data. Discretization

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... analysis, and other data- mining tasks (Hawkins, 1980, Barnett and Lewis, 1994, Ruts and Rousseeuw, 1996, Fawcett and Provost, 1997, Johnson et al., 1998, Penny and Jolliffe, 2001,Acuna and Rodriguez, ... quantitative data flourish, and the learning algorithms many of which are more adept at learning from qualitative data. Hence, discretization has an important role in Data Mining and knowledge discovery. ... the data- mining methods, also called distance-based methods. These methods are usu- ally based on local distance measures and are capable of handling large databases (Knorr and Ng, 1997, Knorr and

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... When data is limited, it is common practice to re-sample the data, that is, partition the data into training and test sets in different ways. An inducer is trained and tested for each partition and ... is provided. Random sub-sampling and n-fold cross-validation are two common methods of re-sampling. In random subsampling, the data is randomly partitioned into disjoint training and test sets ... basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and chal- lenges for data mining tasks. Key

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... programming (Duda and Hart, 1973,Bennett and Mangasarian, 1994), linear discriminant analysis (Duda and Hart, 1973,Friedman, 1977,Sklansky and Wassel, 1981, Lin and Fu, 1983,Loh and Vanichsetakul, ... i ∈dom 1 (a i )AND y=c 2 S     σ y=c 2 S        This measure was extended in (Utgoff and Clouse, 1996) to handle target at- tributes with multiple classes and missing data values. Their ... above, and others, have been conducted by several researchers during the last thirty years, such as (Baker and Jain, 1976, BenBassat, 1978, Mingers, 1989, Fayyad and Irani, 1992, Buntine and Niblett,

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... all dataset can fit in the main memory. Chan and Stolfo (1997) suggest partitioning the datasets into several disjointed datasets, so that each dataset is loaded separately into the memory and ... entire dataset. However, this method also has an upper limit for the largest dataset that can be processed, because it uses a data structure that scales with the dataset size and this data structure ... ) AND a j ∈dom 1 (a j ) S    | S | +    σ a i ∈dom 2 (a i ) AND a j ∈dom 2 (a j ) S    | S | When the first split refers to attribute a i and it splits dom(a i ) into dom 1 (a i ) and

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... 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 ... Determining What Is Interesting Sigal Sahar 603 31 Quality Assessment Approaches in Data Mining Maria Halkidi, Michalis Vazirgiannis 613 32 Data Mining Model Comparison Paolo Giudici 641 33 Data Mining ... Contents XIII 54 Collaborative Data Mining Steve Moyle 1029 55 Organizational Data Mining Hamid R. Nemati, Christopher D. Barko 1041 56 Mining Time Series Data Chotirat Ann Ratanamahatana,...

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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. ... goals, and also on the previous steps. There are two major goals in Data Mining: prediction and description. Prediction is often referred to as supervised Data Mining, while descriptive Data Mining includes ... now avail- able to the researchers and practitioners. No one method is superior to others for all cases. The handbook of Data Mining and Knowledge Discovery from Data aims to organize all significant...

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... 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...

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... (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 ... called Landmark MDS (LMDS) (Silva and Tenenbaum, 2002). In LMDS the idea is to choose q points, called ’landmarks’, where q > r (where r is the rank of the distance matrix), but q  m, and to...

Ngày tải lên: 04/07/2014, 05:21