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Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I de Longbing Cao

Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I: Lecture Notes in Computer Science, cartea 6440

Editat de Longbing Cao, Yong Feng, Jiang Zhong
en Limba Engleză Paperback – 5 noi 2010

ACTUALIZAREA: Față de edițiile anterioare, Advanced Data Mining and Applications (ADMA 2010) marchează o tranziție tehnică majoră către gestionarea volumelor masive de date prin platforme cloud și algoritmi capabili să proceseze fluxuri de date variante în timp. Observăm o orientare clară spre soluții pentru „supraîncărcarea informațională”, punând accent nu doar pe înțelegerea datelor, ci pe utilizarea lor imediată în procese decizionale complexe.

Structura volumului este riguros organizată pe metodologii și procese de minerit de date, cuprinsul indicând o progresie de la fundamentele teoretice — precum învățarea incrementală prin ansambluri de tip „bagging” heterogen — la aplicații practice în sisteme cloud (CPLDP). Subliniem prezența unor capitole tehnice dedicate extracției regulilor din Artificial Neural Networks și Support Vector Machines, oferind o perspectivă rară asupra interpretării modelelor de tip „black box”.

Complementar volumului Advances in Knowledge Discovery and Data Mining de Wee Keong Ng, care se concentrează pe tradiția conferințelor PAKDD din regiunea Asia-Pacific, Advanced Data Mining and Applications acoperă nișe emergente la acea dată, precum mineritul datelor biomedicale și spațiale, zone mai puțin explorate în lucrările generaliste. În contextul operei editorului Longbing Cao, acest volum face legătura între cercetarea fundamentală și temele abordate în Agents and Data Mining Interaction, unde accentul cade pe agenți inteligenți și personalizarea soluțiilor pentru utilizatorul final. Dacă în lucrările sale recente, precum cele despre Global COVID-19 Research and Modeling, Longbing Cao aplică modelarea datelor pe crize globale, acest volum din seria Lecture Notes in Computer Science fundamentează algoritmii de bază necesari unor astfel de analize la scară largă.

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Specificații

ISBN-13: 9783642173158
ISBN-10: 3642173152
Pagini: 641
Ilustrații: XIII, 628 p. 221 illus.
Greutate: 0.7 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

De ce să citești această carte

Această resursă este esențială pentru cercetătorii și inginerii de date care studiază evoluția algoritmilor de învățare automată și minerit de date. Cititorul câștigă acces la o selecție riguroasă de 118 lucrări peer-reviewed care detaliază soluții pentru baze de date mari, sisteme cloud și date incerte. Este un punct de referință istoric și tehnic pentru înțelegerea fundamentelor procesării moderne a informației.

Descriere scurtă

With the ever-growing power of generating, transmitting, and collecting huge amounts of data, information overloadis nowan imminent problemto mankind. The overwhelming demand for information processing is not just about a better understanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects ofdata and to help peoplemakeinformed,sensible,and better decisions. At present, growing attention has been paid to the study, development, and application of data mining. As a result there is an urgent need for sophisticated techniques and toolsthat can handle new ?elds of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The 6th International Conference on Advanced Data Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and di?erent applied disciplines. The conference attracted 361 online submissions from 34 di?erent countries and areas. All full papers were peer reviewed by at least three members of the Program Comm- tee composed of international experts in data mining ?elds. A total number of 118 papers were accepted for the conference. Amongst them, 63 papers were selected as regular papers and 55 papers were selected as short papers.

Cuprins

Data Mining Foundations.- Cost Sensitive Classification in Data Mining.- Web Users Access Paths Clustering Based on Possibilistic and Fuzzy Sets Theory.- Discriminative Markov Logic Network Structure Learning Based on Propositionalization and ? 2-Test.- EWGen: Automatic Generation of Item Weights for Weighted Association Rule Mining.- Best Clustering Configuration Metrics: Towards Multiagent Based Clustering.- On Probabilistic Models for Uncertain Sequential Pattern Mining.- Cube Based Summaries of Large Association Rule Sets.- A Perceptron-Like Linear Supervised Algorithm for Text Classification.- Research on Time Series Forecasting Model Based on Moore Automata.- A Clustering Algorithm FCM-ACO for Supplier Base Management.- Nearest Neighbour Distance Matrix Classification.- Classification Inductive Rule Learning with Negated Features.- Fast Retrieval of Time Series Using a Multi-resolution Filter with Multiple Reduced Spaces.- DHPTID-HYBRID Algorithm: A Hybrid Algorithm for Association Rule Mining.- An Improved Rough Clustering Using Discernibility Based Initial Seed Computation.- Fixing the Threshold for Effective Detection of Near Duplicate Web Documents in Web Crawling.- Topic-Constrained Hierarchical Clustering for Document Datasets.- Discretization of Time Series Dataset Using Relative Frequency and K-Nearest Neighbor Approach.- MSDBSCAN: Multi-density Scale-Independent Clustering Algorithm Based on DBSCAN.- An Efficient Algorithm for Mining Erasable Itemsets.- Discord Region Based Analysis to Improve Data Utility of Privately Published Time Series.- Deep Web Sources Classifier Based on DSOM-EACO Clustering Model.- Kernel Based K-Medoids for Clustering Data with Uncertainty.- Frequent Pattern Mining Using Modified CP-Tree for Knowledge Discovery.- SpatialNeighborhood Clustering Based on Data Field.- Surrounding Influenced K-Nearest Neighbors: A New Distance Based Classifier.- A Centroid k-Nearest Neighbor Method.- Mining Spatial Association Rules with Multi-relational Approach.- An Unsupervised Classification Method of Remote Sensing Images Based on Ant Colony Optimization Algorithm.- A Novel Clustering Algorithm Based on Gravity and Cluster Merging.- Data Mining in Specific Areas.- Evolution Analysis of a Mobile Social Network.- Distance Distribution and Average Shortest Path Length Estimation in Real-World Networks.- Self-adaptive Change Detection in Streaming Data with Non-stationary Distribution.- Anchor Points Seeking of Large Urban Crowd Based on the Mobile Billing Data.- Frequent Pattern Trend Analysis in Social Networks.- Efficient Privacy-Preserving Data Mining in Malicious Model.- Analyze the Wild Birds’ Migration Tracks by MPI-Based Parallel Clustering Algorithm.- Formal Concept Analysis Based Clustering for Blog Network Visualization.- Finding Frequent Subgraphs in Longitudinal Social Network Data Using a Weighted Graph Mining Approach.- Weigted-FP-Tree Based XML Query Pattern Mining.- Privacy-Preserving Data Mining in Presence of Covert Adversaries.- Multiple Level Views on the Adherent Cohesive Subgraphs in Massive Temporal Call Graphs.- Combating Link Spam by Noisy Link Analysis.- High Dimensional Image Categorization.- Efficiently Mining Co-Location Rules on Interval Data.- Multiple Attribute Frequent Mining-Based for Dengue Outbreak.- A Top-Down Approach for Hierarchical Cluster Exploration by Visualization.- Distributed Frequent Items Detection on Uncertain Data.- Mining Uncertain Sentences with Multiple Instance Learning.- WeightLOFCC: A Heuristic Weight-Setting Strategy of LOF Applied to OutlierDetection in Time Series Data.- TGP: Mining Top-K Frequent Closed Graph Pattern without Minimum Support.- Research on Similarity Matching for Multiple Granularities Time-Series Data.- A Novel Algorithm for Hierarchical Community Structure Detection in Complex Networks.- Investigating Sequential Patterns of DNS Usage and Its Applications.- Key Issues and Theoretical Framework on Moving Objects Data Mining.- An Improved KNN Based Outlier Detection Algorithm for Large Datasets.- Some Developments of Determinacy Analysis.- A New Computational Framework for Gene Expression Clustering.- Forecasting Short-Term Trends of Stock Markets Based on Fuzzy Frequent Pattern Tree.- Inspired Rule-Based User Identification.