data mining and information security

... Apostolou, Andreas Abecker and Ron Young Knowledge Asset Management 1-85233-583-1 Michalis Vazirgiannis, Maria Halkidi and Dimitrios Gunopulos Uncertainty Handling and Quality Assessment in Data Mining ... learning and mining see Learning and mining algorithm Ali, K 27, 181 Allen, J 89, 188 Allen, L 160, 197 Alves-Foss, J 15, 117, 180, 192 Anderberg, M.R 96, 97, 189 Anderson, D 160, 196 Anderson, ... Methods for Knowledge Discovery from Complex Data 1-85233-989-6 Marcus A Maloof (Ed.) Machine Learning and Data Mining for Computer Security Methods and Applications With 23 Figures Marcus A Maloof,

Ngày tải lên: 07/09/2020, 13:19

... 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 ... (Galhardas, 2001) data cleansing is the process of eliminating the errors and the inconsistencies in data and solving the object identity problem. Hernandez and Stolfo (1998) define the data cleansing

Ngày tải lên: 04/07/2014, 05:21

... 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. ... from incomplete information systems. Pro- ceedings of the Workshop on Foundations and New Directions in Data Mining, asso- ciated with the third IEEE International Conference on Data Mining, Melbourne,

Ngày tải lên: 04/07/2014, 05:21

... incomplete information databases. ACM Transactions on Database Systems 4 (1979), 262–296. Lipski W. Jr. On databases with incomplete information. Journal of the ACM 28 (1981) 41– 70. Little R.J.A. and ... Multivariate Data. Chapman and Hall, London, 1997. Slowinski R. and Vanderpooten D. A generalized definition of rough approximations based on similarity. IEEE Transactions on Knowledge and Data Engineering ... Rough Sets, Data Mining, and Granular-Soft Computing, RSFDGrC’1999, Ube, Yamaguchi, Japan, November 8–10, 1999, 73–81. Stefanowski J. and Tsoukias A. Incomplete information tables and rough classification.

Ngày tải lên: 04/07/2014, 05:21

... 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 ... eigenvectors and eigenvalues depend non-linearly on the data) , and this can severely limit the usefulness of the approach. Several versions of nonlinear PCA have been proposed (see e.g. (Diamantaras and

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- ... point to the landmarks, f. The third term is the row mean of the landmark distance squared matrix, ¯ E. The second and fourth terms are proportional to the vector of all ones e, and can be dropped

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

Ngày tải lên: 04/07/2014, 05:21

... 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, ... 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 ... Rousseeuw, 1990, Ng and Han, 1994, Ramaswamy et al., 2000, Barbara and Chen, 2000, Shekhar and Chawla, 2002, Shekhar and Lu, 2001, Shekhar and Lu, 2002, Acuna and Rodriguez, 2004). Hu and Sung (2003)

Ngày tải lên: 04/07/2014, 05:21

... in large data sets,” IEEE Transactions on Knowl- edge and Data Engineering, 15 (5), 1170-1187, 2003. Liu H., Shah S., Jiang W., ”On-line outlier detection and data cleaning,” Computers and Chemical ... detects and replaces outliers on-line while preserving all other information in the data. The authors demonstrated that the proposed filter-cleaner is efficient in outlier detection and data cleaning ... 1994, Lu and Reynolds, 1999, Runger and Willemain, 1995, Apley and Shi, 1999)), and to parameter-free methods, where the model parameters are only implicitly derived, if at all (Montgomery and Mas-

Ngày tải lên: 04/07/2014, 05:21

... 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

Ngày tải lên: 04/07/2014, 05:21

... 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,

Ngày tải lên: 04/07/2014, 05:21

... 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 ... useful for many application domains, such as: Manufacturing lr18,lr14, Security lr7,l10 and Medicine lr2,lr9, and for many data mining tasks,

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 ... 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 ... 435-444, 1993. Maimon O., and Rokach, L. Data Mining by Attribute Decomposition with semiconductors manufacturing case study, in Data Mining for Design and Manufacturing: Methods and Applications, D.

Ngày tải lên: 04/07/2014, 05:21

... seven cases, and the frequencies n 3 jk . The full joint distribution is defined by the parameters θ 3 jk , and the parameters θ 1k and θ 2k that specify the marginal distributions of Y 1 and Y 2 . ... j  are independent for i  = i and j = j  . These as- sumptions are known as global and local parameter independence (Spiegelhalter and Lauritzen, 1990), and are valid only under the assumption ... with the number of candidate parents and successful heuristic search procedures (both deterministic and stochastic) have been proposed to render the task feasible (Cooper and Herskovitz, 1992,Larranaga

Ngày tải lên: 04/07/2014, 05:21

... and decision makers with useful information in a compact and understand- able format. Data are expected to improve the understanding of institutions, busi- nesses, and citizens of the current state ... micro-components and fail to convey an overall picture of the process underlying the data. A different approach to the analysis of survey data would be to employ Data Mining tools to generate hypothesis and ... (Sebastiani and Ramoni, 2000, Sebastiani and Ramoni, 2001B) to customer profiling (Sebastiani et al., 2000) and bioinformatics (Friedman, 2004,Sebastiani et al., 2004,2). Here we describe two Data Mining

Ngày tải lên: 04/07/2014, 05:21

... (Sutton and Barto, 1999, Cristianini and Shawe-Taylor, 2000, Witten and Frank, 2000,Hand et al., 2001,Hastie et al., 2001,Breiman, 2001b,Dasu and Johnson, 2003), and associated with Data Mining ... which are the values one wants to estimate from the data on hand. However, in repeated independent random samples (or random realizations of the data) , the fitted values will vary less. Conversely, ... 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

Ngày tải lên: 04/07/2014, 05:21

... inference from data, ranking of the hypothesis and experimental planning, we can easily understand the crucial role of DM and KM (see Figure 2). Hypothesis Data and e vidence Data M ining Data A nalysis Experim ... handling data receives, we can say that a new eld is being born, called data engineering. One of the essential notions of data engineering is metadata. It is data about data , i.e., a data description ... the data, the better we can understand such data and the more qualied we are to interpret, explain and utilize such data. Example 2. Without further explanations we are unlikely to understand...

Ngày tải lên: 16/08/2013, 16:24