data mining techniques and applications

... Detection In Online Social Networks: Using data- mining Techniques and Fuzzy Logic i ii Anomaly Detection In Online Social Networks: Using data- mining Techniques and Fuzzy Logic Abstract The Online Social ... Discovery and Data Mining (pp 504-509) ACM Aggarwal, C C (2013) Outlier Analysis Springer Aggarwal, C C., & Wang, H (2010) Graph Data Management and Mining: A Survey of Algorithms and Applications ... Conference on Data Mining Workshops (ICDMW) (pp 244-250) IEEE Ghoting, A., Parthasarathy, S., & Otey, M (2008) Fast Mining of Distance-based Outliers in High-dimensional Datasets Data Mining and Knowledge

Ngày tải lên: 07/08/2017, 15:46

... a data mining query language can be used to specify data mining tasks In particular, we examine how to define data warehouses and data marts in our SQL-based data mining query language, DMQL Data ... for time, item, and location are shared between both the sales and shipping fact tables In data warehousing, there is a distinction between a data warehouse and a data mart A data warehouse collects ... form of data cleaning, as well as data reduction In summary, real-world data tend to be dirty, incomplete, and inconsistent Data preprocessing techniques can improve the quality of the data, thereby

Ngày tải lên: 08/08/2014, 18:22

... data warehousing technology 3.3.4 Metadata Repository Metadata are data about data When used in a data warehouse, metadata are the data that define warehouse objects Figure 3.12 showed a metadata ... between the current detailed data and the lightly summarized data, and between the lightly summarized data and the highly summarized data Metadata should be stored and managed persistently (i.e., ... dimensions, hierarchies, and derived data definitions, as well as data mart locations and contents Operational metadata, which include data lineage (history of migrated data and the sequence of transformations

Ngày tải lên: 08/08/2014, 18:22

... partitioning the data (mining on each partition and then combining the results) and sampling the data (mining on a subset of the data) These variations can reduce the number of data scans required ... Cheung, Han, Ng, and Wong [CHNW96] Parallel and distributed association data mining under the Apriori framework was studied by Park, Chen, and Yu [PCY95b], Agrawal and Shafer [AS96], and Cheung, Han, ... association mining was studied in Han and Fu [HF95], and Srikant and Agrawal [SA95] In Srikant and Agrawal [SA95], such mining was studied in the context of generalized association rules, and an R-interest

Ngày tải lên: 08/08/2014, 18:22

... Recent data mining research has built on such work, developing scalable classification and prediction techniques capable of handling large disk-resident data In this chapter, you will learn basic techniques ... cuboids for city and item, city and year, city and sales, and the 3-D cuboid for item, year, and sales In this way, an iterative technique can be used to build higher-order data cubes from lower-order ... such as binning, histogram analysis, and clustering Data cleaning, relevance analysis (in the form of correlation analysis and attribute subset selection), and data transformation are described

Ngày tải lên: 08/08/2014, 18:22

... subsets D1 , D3 , , Dk and tested on D2 ; and so on Unlike the holdout and random subsampling methods above, here, each sample is used the same number of times for training and once for testing ... of data tuples The bootstrap method works well with small data sets 14 e is the base of natural logarithms, that is, e = 2.718 366 Chapter Classification and Prediction M1 New data sample M2 Data ... long processing times and the intricacies of complex data 7.9 Clustering High-Dimensional Data Most clustering methods are designed for clustering low-dimensional data and encounter challenges

Ngày tải lên: 08/08/2014, 18:22

... items in em )em+1 · · · en 506 Chapter Mining Stream, Time-Series, and Sequence Data Table 8.2 Projected databases and sequential patterns prefix projected database sequential patterns a (abc)(ac)d(c ... prefix b , c , d , e , and f , respectively This can be done by constructing the b -, c -, d -, e -, and f -projected databases and mining them respectively The projected databases as well as the ... Biological Data Bioinformatics is a promising young field that applies computer technology in molecular biology and develops algorithms and methods to manage and analyze biological data Because DNA and

Ngày tải lên: 08/08/2014, 18:22

... retrieval and multidimensional indexing methods, should be integrated with data generalization and data mining techniques to achieve satisfactory results Techniques for mining such data are further ... component of such databases can be generalized, and how the generalized data can be used for multidimensional data analysis and data mining 10.1.1 Generalization of Structured Data An important ... object-relational and object-oriented databases is their capability of storing, accessing, and modeling complex structure-valued data, such as set- and list-valued data and data with nested structures

Ngày tải lên: 08/08/2014, 18:22

... Applications and Trends in Data Mining Coupling data mining with database and/ or data warehouse systems: A data mining system should be coupled with a database and/ or data warehouse system, where ... standardize data mining products and to 11.2 Data Mining System Products and Research Prototypes 663 ensure the interoperability of data mining systems Recent efforts at defining and standardizing data mining ... visualizer, and (multidimensional data) scatter visualizer for the visualization of data and data mining results 664 Chapter 11 Applications and Trends in Data Mining Oracle Data Mining (ODM),

Ngày tải lên: 08/08/2014, 18:22

... the benefits of data mining in terms of time and money savings and the discovery of new knowledge 11.5 Trends in Data Mining The diversity of data, data mining tasks, and data mining approaches ... content mining, Weblog mining, and data mining services on the Internet will become one of the most important and flourishing subfields in data mining Distributed data mining: Traditional data mining ... issues in data mining The development of efficient and effective data mining methods and systems, the construction of interactive and integrated data mining environments, the design of data mining

Ngày tải lên: 08/08/2014, 18:22

... and lots of data with few measurement errors This data changes over time, and values are sometimes incomplete The data miner has to be particularly suspicious about bias introduced into the data ... dive into more detail into more modern techniques for building models and understanding data Many of these techniques have been adopted by statisticians and build on over a century of work in ... test; some­ times these hitches make it impossible to read the results Data Is Censored and Truncated The data used for data mining is often incomplete, in one of two special ways Censored values

Ngày tải lên: 14/08/2014, 11:21

... commercial data mining software packages The problem is that just by breaking the larger data set into many small subsets , the number of classes represented in each node tends to go down, and with ... Breiman, Jerome Friedman, Richard Olshen, and Charles Stone in 1984 The acronym stands for Classification and Regression Trees The CART algorithm grows binary trees and continues splitting as long as ... complexity and training set misclassification rate The CART algorithm identifies a set of such subtrees as candidate models These candidate subtrees are applied to the validation set and the tree

Ngày tải lên: 14/08/2014, 11:21

... of information out of a few hundred data points In data mining applications, the volumes of data are so large that statistical con­ cerns about confidence and accuracy are replaced by concerns ... is a start and stop, a decision that often depends on the type of busi­ ness and available data The second challenge is technical: finding these start and stop dates in available data may be ... failures In the world of customers, tens of thousands is the lower limit, since cus­ tomer databases often contain data on millions of customers and former customers Much of the statistical background

Ngày tải lên: 14/08/2014, 11:21

... of techniques to apply in a particular situation depends on the nature of the data mining task, the nature of the available data, and the skills and preferences of the data miner. Data mining ... By data mining, of course! How Data Mining Was Applied Most data mining methods learn by example. The neural network or decision tree generator or what have you is fed thousands and thousands ... that, on a technical level, the data mining effort is working and the data is reasonably accurate. This can be quite comforting. If the data and the data mining techniques applied to it are powerful...

Ngày tải lên: 07/04/2014, 11:16