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A Study on Various Methods for Mining Energy Consumption pattern from Smart Home Big Data

Jemeema Maria John, Hima Anns Roy
2018 IOP Conference Series: Materials Science and Engineering  
The techniques that are used for mining and predicting energy consumption patterns are mentioned in this paper.  ...  Data mining helps in easily predicting the consumption patterns of various equipments. Forecasting energy consumption pattern helps in better energy management plans.  ...  of labeling to multi- label data is expensive and time consuming Rule mining and DBSCAN clustering • 5 1234567890''""  ... 
doi:10.1088/1757-899x/396/1/012023 fatcat:qjv6b3sqkbajjoab7bshukpsre

Similarity Measure Selection for Clustering Time Series Databases using Leading Activities

T. Karthikeyan, Dr. T. Sitamahalakshmi
2017 International Journal of Engineering Research and  
Data mining is considered to be the scrutinizing process of "knowledge discovery in databases" process, or KDD. A series of information points are indexed (tabulated or portrayed) in time order.  ...  It's a laborious task to extract all data from the given set of the time series. This paper concentrates on the comparative study of different papers and proposes a typical solution for the drawbacks.  ...  Data mining is done for analysing various techniques or methods in time series [2] . Thus, it provides a good comprehension of the time series in data mining research field.  ... 
doi:10.17577/ijertv6is050557 fatcat:stdq4m2qpfcjvnyegd7zrv6nwq

Deep Learning Based Anomaly Detection for Muti-dimensional Time Series: A Survey [chapter]

Zhipeng Chen, Zhang Peng, Xueqiang Zou, Haoqi Sun
2022 Communications in Computer and Information Science  
However, multi-dimensional time series have problems such as dimensional explosion and data sparseness, as well as complex pattern features such as periods and trends.  ...  In the big data scenario, deep learning method begins to be applied to anomaly detection tasks for multi-dimensional time series due to its wide coverage and strong learning ability.  ...  Therefore, for multi-dimensional time series data, it is necessary to mine spatial semantics in spatial dimensions.  ... 
doi:10.1007/978-981-16-9229-1_5 fatcat:te5p2vz2hfcu7cydy3vrq7fkye

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Hadi Banaee, Mobyen Ahmed, Amy Loutfi
2013 Sensors  
In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series  ...  This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services.  ...  persons' independence and participation in the self-serve society.  ... 
doi:10.3390/s131217472 pmid:24351646 pmcid:PMC3892855 fatcat:fy4hhounsrgffno2fclhanqt7m

Real-Time Medical Electronic Data Mining Based Hierarchical Attention Mechanism

Yi Mao, Yun Li, Yixin Chen
2020 ICIC Express Letters  
time series.  ...  In this paper, we recur to hierarchical attention and encoder-to-decoder based model to automatically learn features from medical records of time series of vital sign, categorical features which include  ...  Chen et al. employed multi-scale convolutional neural networks for time series in [13] . Mao et al. [14] proposed an integrated data mining approach with the multi-statistical features.  ... 
doi:10.24507/icicel.14.12.1155 fatcat:hqbycbrvknbltmihden2hgl6v4

Learning Multi-level Features For Sensor-based Human Action Recognition [article]

Yan Xu, Zhengyang Shen, Xin Zhang, Yifan Gao, Shujian Deng, Yipei Wang, Yubo Fan, Eric I-Chao Chang
2017 arXiv   pre-print
This paper proposes a multi-level feature learning framework for human action recognition using a single body-worn inertial sensor.  ...  Low-level features capture the time and frequency domain property while mid-level representations learn the composition of the action.  ...  Approximately, given fixed-length sampling series, in the first (low-level) stage, the complexity depends on types of low-level features extracted.  ... 
arXiv:1611.07143v2 fatcat:zart252bardbbhs2ul4vp7seju

Music Data Mining [chapter]

Tao Li, Lei Li
2011 Chapman & Hall/CRC Data Mining and Knowledge Discovery Series  
For music data, feature extraction involves low-level musical feature extraction (e.g., acoustic features) and high-level features of musical feature extraction(e.g., music keys).  ...  Kameoka et al. decompose the energy patterns diffused in time frequency space, i.e., a time series of power spectrum, into distinct clusters such that each of them is originated from a single sound stream  ...  Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22 (2)  ... 
doi:10.1201/b11041-3 fatcat:y2etjljj6jdzrkq7dzikyk5kwq

Research on Domain Information Mining and Theme Evolution of Scientific Papers [article]

Changwei Zheng, Zhe Xue, Meiyu Liang, Feifei Kou, Zeli Guan
2022 arXiv   pre-print
This paper introduces the research status at home and abroad in terms of domain information mining and topic evolution law of scientific and technological papers from three aspects: the semantic feature  ...  It is difficult to effectively analyze today's scientific research results when looking at a single research field in isolation.  ...  LSTNet [60] employs Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to extract short-term local dependency patterns among variables and discover long-term patterns of time series  ... 
arXiv:2204.08476v1 fatcat:7cte3exhajbilbkvhktjgyvqha

Automated Spatiotemporal Classification Based on Smartphone App Logs

Shinjin Kang, Youngbin Kim, Sookyun Kim
2020 Electronics  
In this paper, a framework for user app behavior analysis using an automated supervised learning method in smartphone environments is proposed.  ...  The server learns the representative trajectory patterns of the user by combining the collected app usage patterns and trajectory data.  ...  Time Series Repetitive App Patterns The app usage details in a usage area may differ depending on the collection time.  ... 
doi:10.3390/electronics9050755 fatcat:vgbkmggupff3xisvkgzxjsci3y

Pattern recognition in multivariate time series

Stephan Spiegel, Brijnesh Johannes Jain, Ernesto William De Luca, Sahin Albayrak
2011 Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management - PIKM '11  
This dissertation proposal aims at developing and investigating efficient methods for the recognition of contextual patterns in multivariate time series in different application domains based on machine  ...  To this end, we propose a generic three-step approach that involves (1) feature extraction to build robust learning models based on significant time series characteristics, (2) segmentation to identify  ...  In the context of pattern recognition in multivariate time series, feature extraction can be considered as a preprocessing step for further data mining and machine learning algorithms.  ... 
doi:10.1145/2065003.2065011 dblp:conf/cikm/SpiegelJLA11 fatcat:nlyhocoq6va6lcoczhj3rub6xy

MINE: A method of Multi-Interaction heterogeneous information Network Embedding

Dongjie Zhu, Yundong Sun, Xiaofang Li, Haiwen Du, Rongning Qu, Pingping Yu, Xuefeng Piao, Russell Higgs, Ning Cao
2020 Computers Materials & Continua  
Firstly, we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.  ...  Interactivity is the most significant feature of network data, especially in social networks.  ...  Multi-interaction network representation method and multi-interaction sequence extraction algorithm Figure 3: Schematic diagram of hybrid interactive sequence extraction To better explore the complex  ... 
doi:10.32604/cmc.2020.010008 fatcat:zexmrpvbxncozcpqi4jsmbvxmq

A review of EO image information mining [article]

Marco Quartulli, Igor G. Olaizola
2012 arXiv   pre-print
The approaches taken are analyzed, focusing in particular on the phases after primitive feature extraction.  ...  The solutions envisaged for the issues related to feature simplification and synthesis, indexing, semantic labeling are reviewed. The methodologies for query specification and execution are analyzed.  ...  In Guo et al. (2009) , to better capture semantic features for object discovery, a hyperclique pattern discovery method is exploited to find in the DB co-occurrence patterns representing complex objects  ... 
arXiv:1203.0747v2 fatcat:nwiylcsdrnhthi753xcxwxgo7e

A review of heterogeneous data mining for brain disorder identification

Bokai Cao, Xiangnan Kong, Philip S. Yu
2015 Brain Informatics  
They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, and multi-view feature analysis.  ...  In this paper, we review some recent data mining methods that are used for analyzing brain disorders.  ...  Acknowledgments This work is supported in part by NSF through grants III-1526499, CNS-1115234, and OISE-1129076, and Google Research Award.  ... 
doi:10.1007/s40708-015-0021-3 pmid:27747561 pmcid:PMC4883173 fatcat:rhvqh4vmeffnnoxts7esxwxlsq

A review of heterogeneous data mining for brain disorders [article]

Bokai Cao, Xiangnan Kong, Philip S. Yu
2015 arXiv   pre-print
They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, multi-view feature analysis.  ...  In this paper, we review some recent data mining methods that are used for analyzing brain disorders.  ...  random walks of labeled graphs [30] ; cyclic pattern kernels for graphs count pairs of matching cyclic/tree patterns in two graphs [23] .  ... 
arXiv:1508.01023v1 fatcat:e6nscurzmbc23f26p2q42ccbrm

A study on video data mining

V. Vijayakumar, R. Nedunchezhian
2012 International Journal of Multimedia Information Retrieval  
Data mining is a process of extracting previously unknown knowledge and detecting the interesting patterns from a massive set of data.  ...  Compared to the mining of other types of data, video data mining is still in its infancy. There are many challenging research problems existing with video mining.  ...  They are (i) The extraction of patterns from a multi-symbol stream, and (ii) the extraction of periodic patterns in time-series data.  ... 
doi:10.1007/s13735-012-0016-2 fatcat:xuuf3w3b2rfcxlyevzndz6v62e
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