TY - GEN
T1 - Simulation and analysis of classification optimization model of temperature sensing big data in intelligent building
AU - Zhang, Fuquan
AU - Mao, Zijing
AU - Ding, Gangyi
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/8/15
Y1 - 2016/8/15
N2 - The temperature sensor network in intelligent building classified collection of big data processing has the problem of big data redundancy interference, which results in unable to determine the fixed filter thresholds. This paper proposed Chaos differential disturbance based fuzzy C-means clustering model for big temperature sensing data classification tasks. It requires to analyze temperature sensor in the intelligent building big distributed structure model of data in a database storage system, the big data information flow feature fusion and time series analysis. Based on traditional fuzzy c-means clustering processing, we introduced chaos disturbance to avoid the classification into local convergence and local optimum, and therefore improve the performance of data clustering. The testing results show that our proposed classification method effectively reduces the error rate for classification tasks of temperature data in intelligent building and have achieved the best performance among the existing algorithms.
AB - The temperature sensor network in intelligent building classified collection of big data processing has the problem of big data redundancy interference, which results in unable to determine the fixed filter thresholds. This paper proposed Chaos differential disturbance based fuzzy C-means clustering model for big temperature sensing data classification tasks. It requires to analyze temperature sensor in the intelligent building big distributed structure model of data in a database storage system, the big data information flow feature fusion and time series analysis. Based on traditional fuzzy c-means clustering processing, we introduced chaos disturbance to avoid the classification into local convergence and local optimum, and therefore improve the performance of data clustering. The testing results show that our proposed classification method effectively reduces the error rate for classification tasks of temperature data in intelligent building and have achieved the best performance among the existing algorithms.
KW - Big data
KW - Classification
KW - Intelligent building
KW - Temperature sensor
UR - http://www.scopus.com/inward/record.url?scp=85032940720&partnerID=8YFLogxK
U2 - 10.1145/2955129.2955190
DO - 10.1145/2955129.2955190
M3 - Conference contribution
AN - SCOPUS:85032940720
SN - 9781450341295
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 3rd Multidisciplinary International Social Networks Conference, SocialInformatics 2016, Data Science 2016, MISNC, SI, DS 2016
PB - Association for Computing Machinery
T2 - 3rd Multidisciplinary International Social Networks Conference, MISNC 2016, 5th ASE International Conference on Social Informatics, SocialInformatics 2016 and 7th ASE International Conference on Data Science, DS 2016
Y2 - 15 August 2016 through 17 August 2016
ER -