sequence data mining techniques and applications

... ONLINE SOCIAL NETWORKS: USING DATA- MINING TECHNIQUES AND FUZZY School of Electrical Engineering and Computer Science Faculty of Science and Engineering Queensland University of Technology November ... be developed and customised for a specific application by adopting notions from different disciplines such as statistics, machine learning, and data mining Depending on applications and their limitations, ... interested friends and acquaintances, and to share information, photos, and videos This powerful phenomenon, which captures the structure and dynamics of person-to-person and person-to-technology

Ngày tải lên: 07/08/2017, 15:46

... Case-Based Data Mining Platform 29 Data mining practice in industry heavily depends on experienced data mining professionals to provide solutions For the rarity of data mining professionals, data mining ... simplify data mining process, reduce the dependency on data mining professional, and shorten business decision time Keywords: Data Mining, Knowledge Reuse, Case-Based Reasoning, Case- Based Data Mining ... of the complexity of data mining process and data mining tools In this paper, we propose a case-based data mining platform, which reuses the knowledge captured in past data mining cases to semi-automatically

Ngày tải lên: 05/11/2019, 14:12

... 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

... with data mining technology Data warehouses and data marts are used in a wide range of applications Businessexecutives use the data in data warehouses and data marts to perform data analysis andmake ... respect to datamining functionality and the complexity of the data handled Because data mining involves more automated and deeper analysis than OLAP,data mining is expected to have broader applications ... sense, data mining goes one step beyond traditional on-line analyticalprocessing An alternative and broader view of data mining may be adopted in which datamining covers both data description and data

Ngày tải lên: 08/08/2014, 18:22

... among data.Moreover, it helps in data classification, clustering, and other data mining tasks as well.Thus, frequent pattern mining has become an important data mining task and a focusedtheme in data ... include data cube–based data aggregation and attribute-oriented induction From a data analysis point of view, data generalization is a form of descriptive data mining Descriptive data mining describes ... Data Cube Computation and Data Generalizationcube computation in MOLAP was proposed in Zhao, Deshpande, and Naughton[ZDN97] Ross and Srivastava [RS97] developed a method for computing sparsedata

Ngày tải lên: 08/08/2014, 18:22

... assuming a small data size Recent data mining research has built onsuch work, developing scalable classification and prediction techniques capable of han-dling large disk-resident data In this chapter, ... such as binning, histogram analysis, and clustering Data cleaning, relevance analysis (in the form of correlation analysis and attributesubset selection), and data transformation are described in ... training setsinclude SLIQ and SPRINT, both of which can handle categorical and continuous-valued attributes Both algorithms propose presorting techniques on disk-resident datasets that are too large

Ngày tải lên: 08/08/2014, 18:22

... Michalski,Carbonell, and Mitchell [MCM83,MCM86], Kodratoff and Michalski [KM90], Shavlikand Dietterich [SD90], and Michalski and Tecuci [MT94] For a presentation of machinelearning with respect to data mining applications, ... be found in Quinlan and Rivest [QR89], Mehta, Agrawal, andRissanen [MRA95], and Rastogi and Shim [RS98] Other methods include Niblett andBratko [NB86], and Hosking, Pednault, and Sudan [HPS97] ... functions(Hanson and Burr [HB88]), dynamic adjustment of the network topology (Me´zardand Nadal [MN89], Fahlman and Lebiere [FL90], Le Cun, Denker, and Solla [LDS90],and Harp, Samad, and Guha [HSG90] ), and

Ngày tải lên: 08/08/2014, 18:22

... basic concepts and techniques of data mining The techniques studied, however, were for simple and structured data sets, such as data in relationaldatabases, transactional databases, and data warehouses ... telecommu-nications data, transaction data from the retail industry, and data from electric powergrids Traditional OLAP and data mining methods typically require multiple scans ofthe data and are therefore ... andadvanced database system technologies, and the World Wide Web Therefore, an increas-ingly important task in data mining is to mine complex types of data Furthermore, manydata mining applications

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 ... (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 ... 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

Ngày tải lên: 08/08/2014, 18:22

... in spatial databases Such mining demands an integration of data mining with spatial database technologies It can be used for standing spatial data, discovering spatial relationships and relationships ... spatial data mining techniques due to the huge amount under-of spatial data and the complexity of spatial data types and spatial access methods “What about using statistical techniques for spatial data ... sensing data, and computer tomography It is important to explore data mining in raster or image databases Methodsfor mining raster and image data are examined in the following section regarding themining

Ngày tải lên: 08/08/2014, 18:22

... biological data mining,mining software bugs, Web mining, distributed and real-time mining, graph mining,social network analysis, multirelational and multidatabase data mining, data privacyprotection, and ... theory, and inductivedatabases com-Visual data mining integrates data mining and data visualization in order to discover implicit and useful knowledge from large data sets Forms of visual data mining ... constraint-basedmining), the integration of data mining with data warehousing and database systems,the standardization of data mining languages, visualization methods, and new meth-ods for handling complex data

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

... rules: Watch the game and home team wins and out with friends then beer Watch the game and home team wins and sitting at home then diet soda Watch the game and home team loses and out with friends ... less data preparation than many other techniques because they are equally adept at handling continuous and categorical vari­ables Categorical variables, which pose problems for neural networks and ... Time-oriented data does require a lot of data preparation Time series data must be enhanced so that trends and sequential patterns are made visible Decision trees reveal so much about the data to which

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 ... Automatic cluster detection is an undirected data mining technique that can be used to learn about the structure of complex databases By breaking com­ plex datasets into simpler clusters, automatic

Ngày tải lên: 14/08/2014, 11:21