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Titlebook: Data Analysis and Pattern Recognition in Multiple Databases; Animesh Adhikari,Jhimli Adhikari,Witold Pedrycz Book 2014 Springer Internatio

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書目名稱Data Analysis and Pattern Recognition in Multiple Databases
編輯Animesh Adhikari,Jhimli Adhikari,Witold Pedrycz
視頻videohttp://file.papertrans.cn/263/262655/262655.mp4
概述Recent research on Data Analysis and Pattern Recognition in Multiple Databases.Application of Intelligent Systems Modeling to Multiple Database Analysis.Written by experts in the field.Includes supple
叢書名稱Intelligent Systems Reference Library
圖書封面Titlebook: Data Analysis and Pattern Recognition in Multiple Databases; Animesh Adhikari,Jhimli Adhikari,Witold Pedrycz Book 2014 Springer Internatio
描述.Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patt
出版日期Book 2014
關(guān)鍵詞Data Analysis; Intelligent Systems; Multiple Databases; Pattern Recognition
版次1
doihttps://doi.org/10.1007/978-3-319-03410-2
isbn_softcover978-3-319-37727-8
isbn_ebook978-3-319-03410-2Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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Clustering Local Frequency Items in Multiple Data Sources, based on local frequency items. A multi-branch company transacting from different branches often needs to extract global patterns from data distributed over the branches. Global decisions could be made effectively using such patterns. Thus it becomes important to cluster local frequency items in mu
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Mining Calendar-Based Periodic Patterns in Time-Stamped Data,ant task. Recently, researchers have reported an algorithm for finding calendar-based periodic pattern in a time-stamped data and introduced the concept of certainty factor in association with an overlapped interval. In this chapter, we have extended the concept of certainty factor by incorporating
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Measuring Influence of an Item in Time-Stamped Databases,expressing influence of items on other items. In this chapter, we introduce the notion of an overall influence of a set of items on another set of items. We also propose an extension to the notion of overall association between two items in a database. Using this notion, we have designed two algorit
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Summary and Conclusions,resting as well as challenging when we are required to identify patterns and associations in multiple large data sources. While dealing with the domain of multiple large data sources, it has been observed that many patterns are specific to this domain; also some patterns are extensions of classical
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