data mining techniques and algorithms ppt

... 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 ... online social networks data graph, including modelling, algorithms, labelling, and evaluation Anomaly Detection In Online Social Networks: Using data- mining Techniques and Fuzzy Logic iii In

Ngày tải lên: 07/08/2017, 15:46

... assuming a small data size 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, ... 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 ... to the various classification and prediction methods presented Recent data mining research has contributed to the development of scalable algorithms for classification and prediction Additional contributions

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

... 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

... this watermark DATA STREAMS: MODELS AND ALGORITHMS t... Management of Data [I 61 Deshpande A., Guestrin C., Madden S R., Hellerstein J M and Hong W (2004) Model-Driven Data Acquisition ... International Conference on Data. .. watermark 350 DATA STREAMS: MODELS AND ALGORITHMS Proceedings of the 17th International Conference on ScientiJic and Statistical Database Management [25] ... distributed data streams In ACM SIGMOD '03 International Conference on Management o Data, 2003 f [24] J Widom and R Motwani Query processing, resource management, and approximation in a data

Ngày tải lên: 15/12/2013, 13:15

... fuzzy logic algorithms and methodology:  Ambiguity and Social Judgment: Fuzzy Set Model and Data Analysis  From Fuzzy Datalog to Multivalued Knowledge-Base  Resolution Principle and Fuzzy ... This book presents Algorithms, Techniques, and Implementations of fuzzy logic. It is categorized into two sections, namely: 1. Hybrid Fuzzy Logic Algorithms 2. Techniques and Implementation ... FUZZY LOGIC – ALGORITHMS, TECHNIQUES AND IMPLEMENTATIONS Edited by Elmer P. Dadios           Fuzzy Logic – Algorithms, Techniques and Implementations Edited by

Ngày tải lên: 27/06/2014, 00:20

... 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

... technology in molecular biology and develops algorithms and methods to manage and analyze biological data Because DNA and protein sequences are essential biological data and exist in huge volumes as ... 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 ... time-related sequence data, further development of efficient algorithms for mining various kinds of periodic patterns in sequence databases is desired 8.4 Mining Sequence Patterns in Biological Data Bioinformatics

Ngày tải lên: 08/08/2014, 18:22

... itemset stream mining; the Hoeffding tree, VFDT, and CVFDT algorithms for stream data classification; and the STREAM and CluStream algorithms for stream data clustering A time-series database consists ... 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 ... (OLAP) in such data warehouses, and (2) develop effective and scalable methods for mining knowledge from object databases and/ or data warehouses The second task is largely covered by the mining of

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 ... difficulties using the data stored in database systems and handling large data sets efficiently In data mining systems that are loosely coupled with database and data warehouse systems, the data are retrieved

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

... 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 ... independent of the data Data Mining Applications mining techniques used to generate the scores It is worth noting, however, that many of the data mining techniques in this book can and have been applied ... cycle, and often suggests further data mining opportunities TE 42 Team-Fly® CHAPTER 3 Data Mining Methodology and Best Practices The preceding chapter introduced the virtuous cycle of data mining

Ngày tải lên: 14/08/2014, 11:21

... 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 ... be more receptive to one type of message and some to another Determining Customer Value Customer value calculations are quite complex and although data mining has a role to play, customer value

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

... 9.5 Probabilities of Three Items and Their Combinations COMBINATION PROBABILITY A 45.0 % B 42.5% C 40.0% A and B 25.0 % A and C 20.0 % B and C 15.0% A and B and C 5.0% 309 310 Chapter 9 Table ... P(CONDITION AND RESULT) CONFIDENCE If A and B then C 25% 5% 0.20 If A and C then B 20% 5% 0.25 If B and C then A 15% 5% 0.33 What is confidence really saying? Saying that the rule “if B and C then ... the effort used to apply sophisticated analysis techniques Inexplicable rules are flukes in the data and are not actionable FAMOUS RULES: BEER AND DIAPERS Perhaps the most talked about association

Ngày tải lên: 14/08/2014, 11:21

... Used together, OLAP and data mining reinforce each other’s strengths and provide more opportunities for exploiting data Where Data Mining Fits in with Data Warehousing Data mining plays an important ... partners: ■ ■ Data mining thrives on large amounts of data and the more detailed the data, the better? ?data that comes from a data warehouse ■ ■ Data mining thrives on clean and consistent data? ??capitalizing ... to data mining? ??preparing the data and understanding the results are much more important Databases, such as Oracle and Microsoft SQL Server, are increasingly providing support for data mining algorithms,

Ngày tải lên: 14/08/2014, 11:21

... IT-based data mining projects Building the Data Mining Environment Building a Data Mining Group in the Business Units The alternative to putting the data mining group where the data and comput­ ... all this requires a data mining group and the infrastructure to support it The Data Mining Group The data mining group is specifically responsible for building models and using data to learn about ... benefits and drawbacks, as discussed below Outsourcing Data Mining Companies have varying reasons for considering outsourcing data mining For some, data mining is only an occasional need and so

Ngày tải lên: 14/08/2014, 11:21

... way of storing and accessing data. However, much of their design is focused on updating the data and handling large numbers of transactions. Data mining is interested in combining data together ... message to offer. It can be and is just about everything in between. Data warehousing is a natural ally of data mining. Data mining seeks to find actionable patterns in data and therefore has a firm ... the data ware- house includes support for data mining applications, the warehouse facilitates and catalyzes data mining efforts. The two technologies work together to deliver value. Data mining...

Ngày tải lên: 21/06/2014, 21:20

... 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