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检索条件"任意字段=Conference on Data Mining and Knowledge Discovery - Theory, Tools, and Technology II"

共 841 条记录，以下是71-80 订阅

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9th EAI International conference on Cloud Computing, CloudComp 2019 and the 4th EAI International conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

9th EAI International Conference on Cloud Computing, CloudCo...

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9th EAI International conference on Cloud Computing, CloudComp 2019 and the 4th EAI International conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

ISBN: (纸本)9783030485122

The proceedings contain 55 papers. The special focus in this conference is on Cloud Computing. The topics include: Rendering of Three-Dimensional Cloud Based on Cloud Computing;distributed Stochastic Alternating Direction Method of Multipliers for Big data Classification;personalized Recommendation Algorithm Considering Time Sensitivity;cloud-Based Master data Platform for Smart Manufacturing Process;a Semi-supervised Classification Method for Hyperspectral Images by Triple Classifiers with data Editing and Deep Learning;A Survey of Image Super Resolution Based on CNN;design and Development of an Intelligent Semantic Recommendation System for Websites;a Lightweight Neural Network Combining Dilated Convolution and Depthwise Separable Convolution;resource Allocation Algorithms of Vehicle Networks with Stackelberg Game;research on Coordination Control theory of Greenhouse Cluster Based on Cloud Computing;a Multi-objective Computation Offloading Method in Multi-cloudlet Environment;anomalous Taxi Route Detection System Based on Cloud Services;collaborative Recommendation Method Based on knowledge Graph for Cloud Services;efficient Multi-user Computation Scheduling Strategy Based on Clustering for Mobile-Edge Computing;Grazing Trajectory Statistics and Visualization Platform Based on Cloud GIS;Cloud-Based AGV Control System;a Parallel Drone Image Mosaic Method Based on Apache Spark;cycleSafe: Safe Route Planning for Urban Cyclists;prediction of Future Appearances via Convolutional Recurrent Neural Networks Based on Image Time Series in Cloud Computing;video knowledge discovery Based on Convolutional Neural Network;time-Varying Water Quality Analysis with Semantical mining technology;a Survey of QoS Optimization and Energy Saving in Cloud, Edge and IoT;data-Driven Fast Real-Time Flood Forecasting Model for Processing Concept Drift;intelligent System Security Event Description Method.

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Network Density of States 19

Network Density of States

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25th ACM SIGKDD International conference on knowledge discovery & data mining (KDD)

作者： Dong, Kun Benson, Austin R. Bindel, David Cornell Univ Ithaca NY 14850 USA

ISBN: (纸本)9781450362016

Spectral analysis connects graph structure to the eigenvalues and eigenvectors of associated matrices. Much of spectral graph theory descends directly from spectral geometry, the study of differentiable manifolds through the spectra of associated differential operators. But the translation from spectral geometry to spectral graph theory has largely focused on results involving only a few extreme eigenvalues and their associated eigenvalues. Unlike in geometry, the study of graphs through the overall distribution of eigenvalues - the spectral density - is largely limited to simple random graph models. The interior of the spectrum of real-world graphs remains largely unexplored, difficult to compute and to interpret. In this paper, we delve into the heart of spectral densities of real-world graphs. We borrow tools developed in condensed matter physics, and add novel adaptations to handle the spectral signatures of common graph motifs. The resulting methods are highly efficient, as we illustrate by computing spectral densities for graphs with over a billion edges on a single compute node. Beyond providing visually compelling fingerprints of graphs, we show how the estimation of spectral densities facilitates the computation of many common centrality measures, and use spectral densities to estimate meaningful information about graph structure that cannot be inferred from the extremal eigenpairs alone.

关键词： spectral graph theory numerical linear algebra

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Quantifying Long Range Dependence in Language and User Behavior to improve RNNs 19

Quantifying Long Range Dependence in Language and User Behav...

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25th ACM SIGKDD International conference on knowledge discovery & data mining (KDD)

作者： Belletti, Francois Chen, Minmin Chi, Ed H. Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781450362016

Characterizing temporal dependence patterns is a critical step in understanding the statistical properties of sequential data. Long Range Dependence (LRD) - referring to long-range correlations decaying as a power law rather than exponentially w.r.t. distance demands a different set of tools for modeling the underlying dynamics of the sequential data. While it has been widely conjectured that LRD is present in language modeling and sequential recommendation, the amount of LRD in the corresponding sequential datasets has not yet been quantified in a scalable and model-independent manner. We propose a principled estimation procedure of LRD in sequential datasets based on established LRD theory for real-valued time series and apply it to sequences of symbols with million-item-scale dictionaries. In our measurements, the procedure estimates reliably the LRD in the behavior of users as they write Wikipedia articles and as they interact with YouTube. We further show that measuring LRD better informs modeling decisions in particular for RNNs whose ability to capture LRD is still an active area of research. The quantitative measure informs new Evolutive Recurrent Neural Networks (EvolutiveRNNs) designs, leading to state-of-the-art results on language understanding and sequential recommendation tasks at a fraction of the computational cost.

关键词： data mining sequential learning long range dependence RNN

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Platform for Adaptive knowledge discovery and Decision Making Based on Big Genomics data Analytics 7th

Platform for Adaptive Knowledge Discovery and Decision Makin...

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7th International Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Borovska, Plamenka Gancheva, Veska Georgiev, Ivailo Tech Univ Sofia Kliment Ohridski 8 Sofia 1000 Bulgaria Stephan Angeloff Inst Microbiol Georgi Bonchev 26 Sofia 1113 Bulgaria

ISBN: (纸本)9783030179359;9783030179342

In the past years, researchers and analysts worldwide determine big data as a revolution in scientific research and one of the most promising trends that has given impetus to the intensive development of methods and technologies for their investigation and has resulted in the emergence of a new paradigm for scientific research data-Intensive Scientific discovery (DISD). The paper presents a platform for adaptive knowledge discovery and decision making tailored to the target of scientific research. The major advantage is the automatic generation of hypotheses and options for decisions, as well as verification and validation utilizing standard data sets and expertise of scientists. The platform is implemented on the basis of scalable framework and scientific portal to access the knowledge base and the software tools, as well as opportunities to share knowledge and technology transfer.

关键词： Big genomic data analytics data integration knowledge discovery from data

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Parallel mining of Top-k High Utility Itemsets in Spark In-Memory Computing Architecture 23rd

Parallel Mining of Top-k High Utility Itemsets in Spark In-M...

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23rd Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Lin, Chun-Han Wu, Cheng-Wei Huang, JianTao Tseng, Vincent S. Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan Natl Ilan Univ Dept Comp Sci & Informat Engn Yilan Taiwan Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9783030161453;9783030161446

Top-k high utility helmet (abbr. Top-k HUI) mining aims at efficiently mining k itemsets having the highest utility without setting the minimum utility thresholds. Although some studies have been conducted on top-k HUI mining recently, they mainly focus on centralized databases and are not scalable for big data environments. To address the above issues, this paper proposes a novel framework for parallel mining of top-k high utility itemsets in big data. Besides, a new algorithm called PKU (Parallel Top-K High Utility Itemset mining) is proposed for parallel mining of top-k HUIs on Spark in-memory platform. It adopts MapReduce architecture to divide the whole mining task into several independent subtasks, and takes good use of Spark in-memory computing technology for efficiently processing data in parallel. Moreover, several novel strategies are also proposed for pruning the redundant candidates such that the execution time and memory usage in the mining process are reduced greatly. The proposed PKU algorithm inherits several advantages of Spark, including low communication cost, fault tolerance, and high scalability. Experimental results on both real and synthetic datasets show that PKU has good scalability and performance on large datasets with outperforming several benchmarking algorithms.

关键词： Top-k high utility itemset In-memory computing Big data MapReduce Spark platform

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A Hash Method for Calculating Rough Set Approximations 14

A Hash Method for Calculating Rough Set Approximations

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14th IEEE International conference on Intelligent Systems and knowledge Engineering, ISKE 2019

作者： Wang, Guoqiang Li, Tianrui Huang, Qianqian Yang, Xin Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Southwestern University of Finance and Economics School of Economic Information Engineering Chengdu611130 China

ISBN: (纸本)9781728123486

Rough set theory (RST) contributes a practical mathematic framework for knowledge discovery and uncertainty reasoning. Hash tables, often employed in data mining, are one of important data models in data structure and can fast access an object according to its key. The computation of rough set approximations plays a crucial role as a basic step of knowledge discovery or relevant tasks under RST. The calculation process of approximations, however, is very time-consuming. To address this issue, through integrating generic information system with highly efficient hash table, the concept of hash information systems (HIS) is firstly introduced and some of their properties are explored. Then, A hash-information-system-based method is proposed for the calculation of rough-set approximations. Finally, This study offers an example for illustrating the validity of the presented approach. © 2019 IEEE.

关键词： data mining

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An Accelerator of Feature Selection Applying a General Fuzzy Rough Model 23rd

An Accelerator of Feature Selection Applying a General Fuzzy...

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23rd Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Ni, Peng Zhao, Suyun Chen, Hong Li, Cuiping Renmin Univ China Key Lab Data Engn & Knowledge Engn MOE Beijing Peoples R China Renmin Univ China Informat Sch Beijing Peoples R China

ISBN: (纸本)9783030161453;9783030161446

Feature selection, also known as variable selection or attribute reduction, is to select a subset relevant features to speedup learning/mining and to improve the learning/mining quality. In the big data era, some feature selection methods have to face the running time problem led by the large-scale data. As a result, in this paper, we try to narrow this gap by proposing a feature selection accelerator. Considering fuzzy rough techniques need no extra expert knowledge, we design the feature selection accelerator based on fuzzy rough reduction techniques. First, we proposed a fuzzy rough accelerator by deleting the learned/discernible instances in the process of feature selection, which decreases the computation and accelerates feature selection. Second, we design a fuzzy rough based feature selection accelerated algorithm. Finally, the numerical experiments demonstrate that the proposed accelerated algorithm could obtain the same reduction results and save much more time, especially on the large-scale datasets.

关键词： Feature selection Fuzzy rough techniques Accelerator Fuzzy positive region

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Jaccard Coefficient-Based Bi-clustering and Fusion Recommender System for Solving data Sparsity 23rd

Jaccard Coefficient-Based Bi-clustering and Fusion Recommend...

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23rd Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Cheng, Jiangfei Zhang, Li Soochow Univ Dept Comp Sci & Technol Suzhou Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou Peoples R China

ISBN: (纸本)9783030161453;9783030161446

Recommender systems have been very common and useful nowadays, which recommend suitable items to users by predicting ratings for items. The most used collaborative filtering recommender system suffers from the sparsity issue due to insufficient data. To cope with this issue, we propose a Jaccard Coefficient-based Bi-clustering and Fusion (JC-BiFu) method for Recommender system. JC-BiFu uses density peak clustering for both users and items, and then makes estimations for missing values in the user-item rating matrix when finding the similar users. Finally, we utilize both users and items to generate the final predictions. Experimental analysis shows that our approach can improve the performance of user recommendations at the extreme levels of sparsity in user-item rating matrix.

关键词： Collaborative filtering Recommender system Jaccard coefficient Cluster data sparsity

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Detection of Desertion Patterns in University Students Using data mining Techniques: A Case Study 4th

Detection of Desertion Patterns in University Students Using...

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4th International conference on technology Trends (CITT)

作者： Vila, Dayana Cisneros, Saul Granda, Pedro Ortega, Cosme Posso-Yepez, Miguel Garcia-Santillan, Ivan Univ Tecn Norte Fac Appl Sci Dept Software Engn Ibarra Ecuador Univ Tecn Norte Fac Educ Sci & Technol Ibarra Ecuador

ISBN: (纸本)9783030055325;9783030055318

Student desertion is a phenomenon that affects higher education and academic quality standards. Several causes can lead to this issue, the academic factor being a potential reason. The main objective of this research is to detect dropout patterns in the "Tecnica del Norte" University (Ecuador), based on personal and academic historical data, using predictive classification techniques in data mining. The KDD (knowledge discovery in databases) process was used to determine desertion patterns focused on two approaches: (i) Bayesian, and (ii) Decision Trees, both implemented on Weka. The classifiers performance was quantitatively evaluated using the confusion matrix and quality metrics. The results proved that the technique based on decision trees had slightly better performance than the Bayesian approach on the processed data.

关键词： Student desertion Pattern discovery data mining KDD Weka

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A Community Detection Approach for Smart-Phone Addiction Recognition 8

A Community Detection Approach for Smart-Phone Addiction Rec...

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8th International conference on data Science, technology and Applications (data)

作者： Cozzolino, Fabio Moscato, Vincenzo Picariello, Antonio Sperli, Giancarlo Univ Naples Federico II DIETI Naples Italy CINI ITEM Natl Lab Naples Italy

ISBN: (纸本)9789897583773

In this paper, we present a novel approach for Smart-Phone Addiction recognition that leverages community detection algorithms from the Social Network Analysis (SNA) theory. Our basic idea is to model data concerning users' behavior while they are using mobile devices as a particular social graph, discovering by means of SNA facilities patterns that better identify users with a high predisposition to smart phone addiction. Eventually, several experiments on a sample of users monitored for several weeks have been carried out to verify effectiveness of the proposed approach in correctly recognizing the related addiction degree.

关键词： Smart-Phone Addiction knowledge discovery Social Network Analysis Community Detection

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