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检索条件"任意字段=2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016"

共 3354 条记录，以下是551-560 订阅

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Pattern Anonymization: Hybridizing data Restructure with Feature Set Partitioning for Privacy Preserving in Supervised Learning 1st

Pattern Anonymization: Hybridizing Data Restructure with Fea...

引用

1st international conference on Computational Intelligence and Informatics (ICCII)

作者： Riyazuddin, M. D. Balaram, V. V. S. S. S. Muffakham Jah Coll Engn & Technol Banjara Hills Hyderabad Telangana India Sreenidhi Inst Sci & Technol Hyderabad Telangana India

ISBN: (纸本)9789811024719;9789811024702

Majority of the data available for knowledge discovery and information retrieval are prone to identity disclosure. The major act to disclose the identity is through exploring the pattern of attributes involved in data formation. The existing benchmarking models are anonymizing the data either by generalizing, deleting the sensitive attributes, or adding noise to the data. Either of these approaches is not guaranteed in optimality and accuracy in results that obtained from the mining models applied on that data set. The deviation in results often causes falsified decision-making, which is unconditionally not acceptable in certain domains like health mining. To fill the gap, here, we proposed a novel hybridization of feature set partitioning and data restructuring to achieve the pattern anonymization. The model is particularly aimed to restructure the data for supervised learning. To the best of our knowledge, pattern anonymization is first of its kind that attempted to anonymize the patterns rather individual attributes. The experiment results also indicate the scope of robustness and scalability of the supervised learning on restructured data.

关键词： Privacy preserving Anonymization Identity disclosure data mining Pattern anonymization data restructuring Feature set partitioning K-anonymity L-diversity

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Review of Clustering Techniques 1st

Review of Clustering Techniques

引用

1st international conference on data Engineering and Communication Technology (ICDECT)

作者： Sreenivasulu, G. Raju, S. Viswanadha Rao, N. Sambasiva ACEEC Dept CSE Hyderabad Andhra Pradesh India JNTUH Hyderabad Andhra Pradesh India SITW Warangal Andhra Pradesh India

ISBN: (纸本)9789811016752;9789811016745

Clustering is the procedure of consortium a set of entities in such a manner those similar entities should in the same group. Cluster analysis is not one specific approach, but the general process to be observed. Clustering can be viewed by different algorithms that differ independently, in their view what is meant by a cluster and how to find them perfectly. Popular notions of clusters include groups with minimum distances among the cluster members. The clustering problem has been discussed by researchers in different things with respective domain. It reveals broad scope of clustering and it is very important in the process of data analysis as one step. However, it is very difficult because of the researchers may assume in different contexts. Clustering is one of best approach of data mining and a common methodology for statistical data analysis. It is used in all major domains like Banking, Health care, Robotics, and other disciplines. This paper mainly aims to discuss about limitations, scope, and purpose of different clustering algorithms in a great detail.

关键词： data mining Clustering (un-supervised) algorithms Categorical data

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Mental Health computing via Harvesting Social Media data 27

Mental Health Computing via Harvesting Social Media Data

引用

27th international Joint conference on Artificial Intelligence (IJCAI)

作者： Jia, Jia Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Minist Educ Beijing Key Lab Pervas Comp Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9780999241127

Mental health has become a general concern of people nowadays. It is of vital importance to detect and manage mental health issues before they turn into severe problems. Traditional psychological interventions are reliable, but expensive and hysteretic. With the rapid development of social media, people are increasingly sharing their daily lives and interacting with friends online. Via harvesting social media data, we comprehensively study the detection of mental wellness, with two typical mental problems, stress and depression, as specific examples. Initializing with binary user-level detection, we expand our research towards multiple contexts, by considering the trigger and level of mental health problems, and involving different social media platforms of different cultures. We construct several benchmark real-world datasets for analysis and propose a series of multi-modal detection models, whose effectiveness are verified by extensive experiments. We also make in-depth analysis to reveal the underlying online behaviors regarding these mental health issues.

关键词： Social networking (online)

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Wireless Sensor Sequence data Model for Smart Home and IoT data Analytics 1st

Wireless Sensor Sequence Data Model for Smart Home and IoT D...

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1st international conference on Computational Intelligence and Informatics (ICCII)

作者： Suryadevara, Nagender Kumar Geethanjali Coll Engn & Technol Hyderabad Telangana India

ISBN: (纸本)9789811024719;9789811024702

In this paper, Wireless sensor sequence data mining model is demonstrated for the smart home and Internet of Things data analytics. Exploration of the sensor data patterns by correlating with the multi stream sensor data that are fused from the wireless sensor network is presented. The effective realization of the sensor data patterns from heterogeneous sensing systems for various applications of IoT can be known from the proposed conceptual data model. The conceptual data model includes the discovering of frequent pattern item sets using various computational archetypal. Results of the explicit patterns augmented for data analytics are encouraging as the prototype was tested through real-time data rather than test bed scenario data or synthetic data.

关键词： Wireless sensor networks Activities of daily living Wellness Smart home Internet of Things data analytics

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data mining based Root-cause Analysis of Performance Bottleneck for Big data Workload 19

Data Mining based Root-cause Analysis of Performance Bottlen...

引用

19th IEEE international conference on High Performance computing and Communications (HPCC) / 15th IEEE international conference on Smart City (SmartCity) / 3rd IEEE international conference on data Science and Systems (DSS)

作者： Qi, Weichen Li, Yunchun Zhou, Hongang Li, Wei Yang, Hailong Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (纸本)9781538625880

Straggler task is commonly considered as the major bottleneck in parallel data processing. Previous work mainly focuses on the coarse-grained straggler detection and optimization such as speculative scheduling. However, fine-grained root-cause analysis of straggler tasks is rarely considered. In addition, existing work simply depends on empirical analysis, which lacks of useful guidance to performance optimization. In this paper, we propose a new methodology of fine-grained straggler root-cause analysis using machine learning. We collect raw metrics from Spark event log and hardware sampling tool, and refine them into high-level metrics for model learning. Then we present the root-cause analysis of stragglers through CART tree. A customized prune method is also applied to improve analysis accuracy. From the analysis, we derive several new findings beyond the well known causes of stragglers. Our work provides a new perspective on identifying and understanding the inefficiency in parallel data processing programs by applying machine learning techniques to fine-grained root-cause analysis of straggler tasks.

关键词： Task analysis Measurement Sparks Hardware Resource management Optimization Big data

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Tweet Cluster Analyzer: Partition and Join-based Micro-clustering for Twitter data Stream 3rd

引用

3rd international conference on Computational Intelligence in data mining (ICCIDM)

作者： Raja, M. Arun Manicka Swamynathan, S. Anna Univ Dept Informat Sci & Technol Coll Engn Guindy Madras 600025 Tamil Nadu India

ISBN: (纸本)9789811038747;9789811038730

data stream mining is the process of extracting knowledge from continuously generated data. Since data stream processing is not a trivial task, the streams have to be analyzed with proper stream mining techniques. In many large volume of data stream processing, stream clustering helps to find the valuable hidden information. Many works have concentrated on clustering the data streams using various methods, but mostly those approaches lack in some core tasks needed to improve the cluster accuracy and quick processing of data streams. To tackle the problem of improving cluster quality and reducing the time for data stream processing time in cluster generation, the partition-based DBStream clustering method is proposed. The result has been compared with various data stream clustering methods, and it is evident from the experiments that the purity of clusters improves 5% and the time taken is reduced by 10% than the average time taken by other methods for clustering the data streams.

关键词： Tweets Micro-clustering Macro-clustering Cosine similarity DBStream Partition Join

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Accelerometer based human activities and posture recognition

Accelerometer based human activities and posture recognition

引用

2016 international conference on data mining and advanced computing, sapience 2016

作者： Babu, Arun Dube, Kudakwashe Mukhopadhyay, Subhas Ghayvat, Hemant Jithin Kumar, M.V. School of Engineering and Advanced Technology Massey University Palmerston North New Zealand Heera College of Engineering and Technology Thiruvananthapuram India

ISBN: (纸本)9781467385947

The quantity of elderly people like to live in their homes, secluded, in their brilliant age is expanding exponentially. This is not a perfect path for an elderly individual to live. However, the urbanization and resultant change of the social and social conduct makes it a more regular event. Falls are a noteworthy reason for death and horribleness in more established grown-ups. In this way, it has turn into an opportune need to create mechanized look after the elderly. The first end, purpose of such fall computer looking-glass is to ready caregivers of the fall event, which can then start an earlier process. In the present study, we amplify the use of wearable inertial sensors for fall identification and information of human posture and activities, by creating and assessing the precision of a sensor framework for identifying the same. We found that our system could discover fall events and monitor human activities with at least 95% accuracy. © 2016 IEEE.

关键词： Fall detection

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BKVex: an Adaptable VLIW Processor and Design Framework for Reconfigurable computing Platforms

BKVex: an Adaptable VLIW Processor and Design Framework for ...

引用

international conference on advanced computing and Applications (ACOMP)

作者： Cuong Pham-Quoc Binh Kieu-Do-Nguyen Anh-Vu Dinh-Duc Ho Chi Minh City Univ Technol VNU HCM Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9781538606070

Reconfigurable computing has been considering as one of the main approaches to utilize billions transistors integrated in a chip in this nanoscale era. In this paper, we introduce our adaptable VLIW processor whose organization is reconfigurable at design time so that the processor is optimized for a particular program or application-domain. Issue-width, the number of registers, instruction and data caches, and functional units availability are reconfigurable parameters. The processor, in all configurations, can function in a 5-stage pipeline model to improve performance. Moreover, we present our design framework that helps users determine an optimized VLIW processor configuration according to requirements of the executed program. The design framework also allows users to implement the VLIW processor on an FPGA-based platform without knowing HDLs. We synthesize the processor in different configurations with four FPGA technologies. The synthesis results show that our processor can work at up to 174.89 MHz with the Xilinx Virtex 7 technology. When compared to NIOS II RISC-like softcore processor at the same working frequency, our adaptable VLIW processor achieves speed-ups by up to 22.6x.

关键词： FPGA Adaptable VLIW processor Issue-width Processor architecture

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Balanced Parallel Frequent Pattern mining Over Massive data Stream 3

Balanced Parallel Frequent Pattern Mining Over Massive Data ...

引用

3rd IEEE international conference on Big data computing Service and Applications (BigdataService)

作者： Fu, Xi Shi, Lei Li, Jing Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Anhui Peoples R China

ISBN: (纸本)9781509063185

Frequent pattern mining is playing an increasingly important role in a growing number of real-time data flow scenarios, such as large-scale order stream data, network traffic monitoring, web accessing record stream, and so on. The continuous, unbounded and high speed characteristics of massive data stream are a huge challenge for the current frequent pattern mining approach. The main challenge is that, as data stream continuously arriving, the non frequent patterns discarded can possibly become frequent again. In this paper, aimed at the characteristics of real-time data stream, we propose a compact data structure, called CPS-tree to maintain and operate the full information of data stream. Compared to current related works, our algorithm can dynamically support large-scale data stream with one-pass scan which can be easily applied to other data stream processing environments;Moreover, the load imbalance in the current frequent pattern mining is a pretty common problem. We analysis the features of data stream, and propose a depth-based strategy to solve the imbalance problem in our parallel algorithm. In conclusion, we propose the BPFPMS algorithm, a balanced parallel frequent pattern mining over massive data stream, to dynamically and efficiently mine frequent patterns over large scale data stream. Our experiments show that our algorithm can achieve a good speedup and a good degree of balance among each node with different degree of parallelism.

关键词： Trees (mathematics)

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Effective Video data Retrieval Using Image Key Frame Selection 1st

Effective Video Data Retrieval Using Image Key Frame Selecti...

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1st international conference on Computational Intelligence and Informatics (ICCII)

作者： Saravanan, D. IFHE Univ Fac Operat & IT Hyderabad 501203 Telangana India

ISBN: (纸本)9789811024719;9789811024702

Owing to the rapid growth of multimedia technology, multimedia information is easily accessed by any user and the same information construction and distribution are also very easy. Due to technology development, the multimedia information increases due to variety of factors: it can be uploaded by unprofessional users nowadays. Due to the low quality and the large number of duplicated video files available, this leads video extraction more and more complex. The general method of representing each video segment is shot that consists of series of frames. Among this series, the input frame based shot method is specifically assisted for searching the video content as clients provided image query/search where an image will be matched with the indexed key frames with assist of resemblance distance. As a result, the key frames selection is most significant, and several methods are used to automate the process. This paper proposes a new technique for key frame selection. The proposed method shows significantly good and the experiments prove the above statement.

关键词： Video data mining data mining Key frame extraction Knowledge extraction Multimedia data data extraction Video dates Frames

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