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Advanced Data Mining and Applications 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I /
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 fash...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2010.
|
| Edición: | 1st ed. 2010. |
| Colección: | Lecture Notes in Artificial Intelligence,
6440 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 245 | 1 | 0 | |a Advanced Data Mining and Applications |h [electronic resource] : |b 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part I / |c edited by Longbing Cao, Yong Feng, Jiang Zhong. |
| 250 | |a 1st ed. 2010. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2010. | |
| 300 | |a XIII, 628 p. 221 illus. |b online resource. | ||
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| 490 | 1 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 6440 | |
| 505 | 0 | |a 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 -- Spatial Neighborhood 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 Outlier Detection 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer networks . | |
| 650 | 0 | |a Application software. | |
| 650 | 0 | |a Information storage and retrieval systems. | |
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Data mining. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Computer Communication Networks. |
| 650 | 2 | 4 | |a Computer and Information Systems Applications. |
| 650 | 2 | 4 | |a Information Storage and Retrieval. |
| 650 | 2 | 4 | |a Database Management. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 700 | 1 | |a Cao, Longbing. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Feng, Yong. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Zhong, Jiang. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642173158 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642173172 |
| 830 | 0 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 6440 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-17316-5 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||