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xiii, 387 p. : ill.
  • Data Mining and Information Systems: Quo Vadis?.- Confirmatory data analysis.- Response-Based Segmentation Using Finite Mixture Partial Least Squares.- Knowledge discovery from supervised learning.- Building Acceptable Classification Models.- Mining Interesting Rules Without Support Requirement: A General Universal Existential Upward Closure Property.- Classification Techniques and Error Control in Logic Mining.- Classification analysis.- An Extended Study of the Discriminant Random Forest.- Prediction with the SVM Using Test Point Margins.- Effects of Oversampling Versus Cost-Sensitive Learning for Bayesian and SVM Classifiers.- The Impact of Small Disjuncts on Classifier Learning.- Hybrid data mining procedures.- Predicting Customer Loyalty Labels in a Large Retail Database: A Case Study in Chile.- PCA-based Time Series Similarity Search.- Evolutionary Optimization of Least-Squares Support Vector Machines.- Genetically Evolved kNN Ensembles.- Web-mining.- Behaviorally Founded Recommendation Algorithm for Browsing Assistance Systems.- Using Web Text Mining to Predict Future Events: A Test of the Wisdom of Crowds Hypothesis.- Privacy-preserving data mining.- Avoiding Attribute Disclosure with the (Extended) p-Sensitive k-Anonymity Model.- Privacy-Preserving Random Kernel Classification of Checkerboard Partitioned Data.
  • (source: Nielsen Book Data)9781441912794 20160605
Over the course of the last twenty years, research in data mining has seen a substantial increase in interest, attracting original contributions from various disciplines including computer science, statistics, operations research, and information systems. Data mining supports a wide range of applications, from medical decision making, bioinformatics, web-usage mining, and text and image recognition to prominent business applications in corporate planning, direct marketing, and credit scoring. Research in information systems equally reflects this inter- and multidisciplinary approach, thereby advocating a series of papers at the intersection of data mining and information systems research. This special issue of Annals of Information Systems contains original papers and substantial extensions of selected papers from the 2007 and 2008 International Conference on Data Mining (DMIN'07 and DMIN'08, Las Vegas, NV) that have been rigorously peer-reviewed. The issue brings together topics on both information systems and data mining, and aims to give the reader a current snapshot of the contemporary research and state of the art practice in data mining.
(source: Nielsen Book Data)9781441912794 20160605
dx.doi.org SpringerLink
1 online resource (xxiv, 572 pages).
  • Classification
  • Machine learning
  • Applications
  • Novel methods and algorithms
  • Opinion mining and sentiment analysis
  • Clustering
  • Feature extraction and pattern mining
  • Graph and network data
  • Spatiotemporal and image data
  • Anomaly detection and clustering
  • Novel models and algorithms
  • Text mining and recommender systems.
This two-volume set, LNAI 9651 and 9652, constitutes the thoroughly refereed proceedings of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, held in Auckland, New Zealand, in April 2016. The 91 full papers were carefully reviewed and selected from 307 submissions. They are organized in topical sections named: classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; feature extraction and pattern mining; graph and network data; spatiotemporal and image data; anomaly detection and clustering; novel models and algorithms; and text mining and recommender systems.
1 online resource (xix, 534 pages) : illustrations.
  • Knowledge discovery and information retrieval
  • Knowledge engineering and ontology development
  • Knowledge management and information sharing.
This book constitutes the thoroughly refereed proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015, held in Lisbon, Portugal, in November 2015. The 25 full papers presented together with 2 invited papers were carefully reviewed and selected from 280 submissions. The papers are organized in topical sections on knowledge discovery and information retrieval; knowledge engineering and ontology development; and knowledge management and information sharing.
1 online resource (xiii, 282 pages) : illustrations.
  • Biologically Inspired Data Mining Techniques, BDM
  • Machine Learning for Sensory Data Analysis, MLSDA
  • Predictive Analytics for Critical Care, PACC
  • Data Mining in Business and Finance, WDMBF.
This book constitutes the thoroughly refereed post-workshop proceedings at PAKDD Workshops 2016, held in conjunction with PAKDD, the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining in Auckland, New Zealand, in April 2016. The 23 revised papers presented were carefully reviewed and selected from 38 submissions. The workshops affiliated with PAKDD 2016 include: Biologically Inspired Data Mining Techniques, BDM; Machine Learning for Sensory Data Analysis, MLSDA; Predictive Analytics for Critical Care, PACC; as well as Data Mining in Business and Finance, WDMBF.
1 online resource (418 pages) : illustrations, graphs.
  • Introduction.- What Data Scientists can Learn from History.- On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies.- PROFIT: A Projected Clustering Technique.- Multi-Label Classification with a Constrained Minimum Cut Model.- On the Selection of Dimension Reduction Techniques for Scientific Applications.- Relearning Process for SPRT In Structural Change Detection of Time-Series Data.- K-means clustering on a classifier-induced representation space: application to customer contact personalization.- Dimensionality Reduction using Graph Weighted Subspace Learning for Bankruptcy Prediction.- Click Fraud Detection: Adversarial Pattern Recognition over 5 years at Microsoft.- A Novel Approach for Analysis of 'Real World' Data: A Data Mining Engine for Identification of Multi-author Student Document Submission.- Data Mining Based Tax Audit Selection: A Case Study of a Pilot Project at the Minnesota Department of Revenue.- A nearest neighbor approach to build a readable risk score for breast cancer.- Machine Learning for Medical Examination Report Processing.- Data Mining Vortex Cores Concurrent with Computational Fluid Dynamics Simulations.- A Data Mining Based Method for Discovery of Web Services and their Compositions.- Exploiting Terrain Information for Enhancing Fuel Economy of Cruising Vehicles by Supervised Training of Recurrent Neural Optimizers.- Exploration of Flight State and Control System Parameters for Prediction of Helicopter Loads via Gamma Test and Machine Learning Techniques.- Multilayer Semantic Analysis In Image Databases.
  • (source: Nielsen Book Data)9783319078113 20160617
Data mining applications range from commercial to social domains, with novel applications appearing swiftly; for example, within the context of social networks. The expanding application sphere and social reach of advanced data mining raise pertinent issues of privacy and security. Present-day data mining is a progressive multidisciplinary endeavor. This inter- and multidisciplinary approach is well reflected within the field of information systems. The information systems research addresses software and hardware requirements for supporting computationally and data-intensive applications. Furthermore, it encompasses analyzing system and data aspects, and all manual or automated activities. In that respect, research at the interface of information systems and data mining has significant potential to produce actionable knowledge vital for corporate decision-making. The aim of the proposed volume is to provide a balanced treatment of the latest advances and developments in data mining; in particular, exploring synergies at the intersection with information systems. It will serve as a platform for academics and practitioners to highlight their recent achievements and reveal potential opportunities in the field. Thanks to its multidisciplinary nature, the volume is expected to become a vital resource for a broad readership ranging from students, throughout engineers and developers, to researchers and academics.
(source: Nielsen Book Data)9783319078113 20160617
1 online resource (xix, 322) : illustrations.
  • AN OVERVIEW OF DATA MINING METHODOLOGIES Introduction to data mining methodologies METHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNS Regression models Bayes classifiers Decision trees Multi-layer feedforward artificial neural networks Support vector machines Supervised clustering METHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNS Hierarchical clustering Partitional clustering Self-organized map Probability distribution estimation Association rules Bayesian networks METHODOLOGIES FOR MINING DATA REDUCTION PATTERNS Principal components analysis Multi-dimensional scaling Latent variable analysis METHODOLOGIES FOR MINING OUTLIER AND ANOMALY PATTERNS Univariate control charts Multivariate control charts METHODOLOGIES FOR MINING SEQUENTIAL AND TIME SERIES PATTERNS Autocorrelation based time series analysis Hidden Markov models for sequential pattern mining Wavelet analysis Hilbert transform Nonlinear time series analysis.
  • (source: Nielsen Book Data)9781482219364 20160618
New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms. The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.
(source: Nielsen Book Data)9781482219364 20160618
p. cm.
  • Application of Data Mining Techniques to Storage Management and Online Distribution of Satellite Images.- A GUI Tool for Plausible Reasoning in the Semantic Web using MEBN.- Multiobjective Optimization and Rule Learning: Subselection Algorithm or Meta-heuristic Algorithm?.- Clustering Dynamic Web Usage Data.- Towards Characterization of the Data Generation Process.- Data Mining Applied to the Electric Power Industry: Classification of Short-Circuit Faults in Transmission Lines.
  • (source: Nielsen Book Data)9783540880448 20160528
Data mining consists of attempting to discover novel and useful knowledge from data, trying to find patterns among datasets that can help in intelligent decision making. However, reports of real-world case studies are not generally detailed in the literature, due to the fact that they are usually based on proprietary datasets, making it impossible to publish the results. This kind of situation makes hard to evaluate, in a precise way, the degree of effectiveness of data mining techniques in real-world applications. On the other hand, researchers of this field of expertise usually exploit public-domain datasets. This volume offers a wide spectrum of research work developed for data mining for real-world application. In the following, we give a brief introduction of the chapters that are included in this book.
(source: Nielsen Book Data)9783540880448 20160528
dx.doi.org SpringerLink
ix, 226 p. : ill. ; 25 cm.
  • Introduction.- Data Mining Process.- Quality Assessment in Data Mining.- Uncertainty Handling in Data Mining.- UMINER: A Data Mining System Handling Uncertainty and Quality.- Case Studies.- Index.
  • (source: Nielsen Book Data)9781852336554 20160528
Uncertainty Handling and Quality Assessment in Data Mining provides an introduction to the application of these concepts in Knowledge Discovery and Data Mining. It reviews the state-of-the-art in uncertainty handling and discusses a framework for unveiling and handling uncertainty. Coverage of quality assessment begins with an introduction to cluster analysis and a comparison of the methods and approaches that may be used. The techniques and algorithms involved in other essential data mining tasks, such as classification and extraction of association rules, are also discussed together with a review of the quality criteria and techniques for evaluating the data mining results. This book presents a general framework for assessing quality and handling uncertainty which is based on tested concepts and theories. This framework forms the basis of an implementation tool, 'Uminer' which is introduced to the reader for the first time. This tool supports the key data mining tasks while enhancing the traditional processes for handling uncertainty and assessing quality. Aimed at IT professionals involved with data mining and knowledge discovery, the work is supported with case studies from epidemiology and telecommunications that illustrate how the tool works in 'real world' data mining projects. The book would also be of interest to final year undergraduates or post-graduate students looking at: databases, algorithms, artificial intelligence and information systems particularly with regard to uncertainty and quality assessment.
(source: Nielsen Book Data)9781852336554 20160528
SAL3 (off-campus storage)

12. Data mining [2018]

120 pages ; 25 cm.
  • Data brokers know all about you / Lois Beckett
  • Big data has the potential to discriminate / Jason Furman and Tim Simcoe
  • Data surveillance is all around us, and it will change our behavior / Uri Gal
  • Surveillance can go too far / National Coalition against Censorship
  • Corporate cybersnooping is a big business / Stephanie Simon and Josh Gerstein
  • Data can improve city life / William Echikson
  • Big data can make a company more efficient / World Economic Forum
  • Data collection will improve health care / Carol McDonald
  • Big data failed to predict the flu / Adam Kucharski
  • Big data helps determine our leaders / Teradata India
  • Government surveillance is a failure / Rachel Levinson-Waldman
  • Data mining won't stop terrorism, but it is one step closer to the police state / American Civil Liberties Union of Massachusetts
  • Big data can help predict crimes / H.V. Jagadish
  • Data mining can be discriminatory / Jeremy Kun
  • Anyonymity on the internet / Mary Madden and Lee Rainie.
In this tech-obsessed day and age, there is no such thing as true security, unless you have no digital footprint whatsoever and live entirely off the grid. A smartphone, a laptop, an online shopping account, all of these things leave users vulnerable to data mining. The viewpoints in this resource debate data mining and whether it's a danger to society or, ultimately, a boon that will only help our technology-driven world grow more efficient. Readers will learn about the benefits of data mining as well as the pitfalls that come with security breaches.
Science Library (Li and Ma)
1 online resource (xxvii, 1722 pages) : illustrations (some color) Digital: text file; PDF.
1 online resource (lviii, 1270 pages) : illustrations (some color)
1 online resource (xiii, 22 pages) : illustrations (chiefly color)
volumes : illustrations ; 28 cm

17. Proceedings [2001 - ]

1 online resource (1 volume) : illustrations


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