TY - GEN
T1 - Fuzzy Based Clustering of Consumers' Big Data in Industrial Applications
AU - Sharma, Akash
AU - Singh, Sunil K.
AU - Badwal, Eshita
AU - Kumar, Sudhakar
AU - Gupta, Brij B.
AU - Arya, Varsha
AU - Chui, Kwok Tai
AU - Santaniello, Domenico
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - As a result of increased miniaturization and sophistication of technology in the field of consumer electronics, the overall demand for these devices has increased. Furthermore, as the world becomes digital, a growing number of individuals save, manage, and share their lives online. Enormous volumes of data from Consumer Electronics constitutes Big Data. To gain insights and valuable information, Clustering of such Big data becomes important in the present scenario. Using innovative ways in clustering and extracting valuable information from this rapidly increasing data in order to provide more personalisation features to users is a crucial step ahead. This paper explores the traditional as well as newer and innovative techniques that have been employed for clustering of big data as well as analytics and their applications to consumer electronics. Fuzzy clustering techniques based on fuzzy membership in which data elements can belong to more than one cluster at a time due to which this technique has an upper hand over conventional clustering algorithms are thus examined in this work. Additionally, this paper presents the drawbacks of fuzzy clustering algorithms and their solutions using Fuzzy-Neuro and Ensemble Clustering based techniques. All of this is being done to advance consumer data analytics via innovation to provide users a better experience.
AB - As a result of increased miniaturization and sophistication of technology in the field of consumer electronics, the overall demand for these devices has increased. Furthermore, as the world becomes digital, a growing number of individuals save, manage, and share their lives online. Enormous volumes of data from Consumer Electronics constitutes Big Data. To gain insights and valuable information, Clustering of such Big data becomes important in the present scenario. Using innovative ways in clustering and extracting valuable information from this rapidly increasing data in order to provide more personalisation features to users is a crucial step ahead. This paper explores the traditional as well as newer and innovative techniques that have been employed for clustering of big data as well as analytics and their applications to consumer electronics. Fuzzy clustering techniques based on fuzzy membership in which data elements can belong to more than one cluster at a time due to which this technique has an upper hand over conventional clustering algorithms are thus examined in this work. Additionally, this paper presents the drawbacks of fuzzy clustering algorithms and their solutions using Fuzzy-Neuro and Ensemble Clustering based techniques. All of this is being done to advance consumer data analytics via innovation to provide users a better experience.
KW - Clustering
KW - Clustering Ensemble
KW - Consumer electronics
KW - Fuzzy
KW - Neuro-fuzzy
UR - http://www.scopus.com/inward/record.url?scp=85149129246&partnerID=8YFLogxK
U2 - 10.1109/ICCE56470.2023.10043451
DO - 10.1109/ICCE56470.2023.10043451
M3 - Conference contribution
AN - SCOPUS:85149129246
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
BT - 2023 IEEE International Conference on Consumer Electronics, ICCE 2023
T2 - 2023 IEEE International Conference on Consumer Electronics, ICCE 2023
Y2 - 6 January 2023 through 8 January 2023
ER -