Analysis of Eight Data Mining Algorithms for Smarter Internet of Things (IoT)

Internet of Things (IoT) is set to revolutionize all aspects of our lives. The number of objects connected to IoT is expected to reach 50 billion by 2020, giving rise to an enormous amounts of valuable data. The data collected from the IoT devices will be used to understand and control complex environments around us, enabling better decision making, greater automation, higher efficiencies, productivity, accuracy, and wealth generation. Data mining and other artificial intelligence methods would play a critical role in creating smarter IoTs, albeit with many challenges. In this paper, we examine the applicability of eight well-known data mining algorithms for IoT data. These include, among others, the deep learning artificial neural networks (DLANNs), which build a feed forward multi-layer artificial neural network (ANN) for modelling high-level data abstractions. Our preliminary results on three real IoT datasets show that C4.5 and C5.0 have better accuracy, are memory efficient and have relatively higher processing speeds. ANNs and DLANNs can provide highly accurate results but are computationally expensive.

[1]  Jagannath Aryal,et al.  Smart city and geospatiality: Hobart deeply learned , 2015, 2015 31st IEEE International Conference on Data Engineering Workshops.

[2]  Cheng Li,et al.  A Feature-Scaling-Based k-Nearest Neighbor Algorithm for Indoor Positioning Systems , 2016, IEEE Internet Things J..

[3]  Shaoning Pang,et al.  Incremental linear discriminant analysis for classification of data streams , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Ying Zhu,et al.  DeepSense: A novel learning mechanism for traffic prediction with taxi GPS traces , 2014, 2014 IEEE Global Communications Conference.

[5]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[6]  Yue Wu,et al.  A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network , 2014, J. Electr. Comput. Eng..

[7]  Guilherme A. Barreto,et al.  Short-term memory mechanisms in neural network learning of robot navigation tasks: A case study , 2009, 2009 6th Latin American Robotics Symposium (LARS 2009).

[8]  Li Ping,et al.  The study of Naive Bayes algorithm online in data mining , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[9]  Athanasios V. Vasilakos,et al.  Data Mining for the Internet of Things: Literature Review and Challenges , 2015, Int. J. Distributed Sens. Networks.

[10]  Angelo Chianese,et al.  An associative engines based approach supporting collaborative analytics in the Internet of cultural things , 2017 .

[11]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[12]  Max Kuhn,et al.  Applied Predictive Modeling , 2013 .

[13]  马华东.  Internet of Things： Objectives and Scientific Challenges , 2011 .

[14]  Mohammed Erritali,et al.  A comparative study of decision tree ID3 and C4.5 , 2014 .

[15]  Zheng Yao,et al.  A Feature-Scaling-Based $k$-Nearest Neighbor Algorithm for Indoor Positioning Systems , 2014, IEEE Internet of Things Journal.

[16]  J. Gerring.  A case study , 2011, Technology and Society.

[17]  Tao Li,et al.  Using discriminant analysis for multi-class classification: an experimental investigation , 2006, Knowledge and Information Systems.

[18]  Hugo Fuks,et al.  Wearable Computing: Accelerometers' Data Classification of Body Postures and Movements , 2012, SBIA.

[19]  S. M. Kamruzzaman,et al.  A New Data Mining Scheme Using Artificial Neural Networks , 2011, Sensors.

[20]  Liangxiao Jiang,et al.  A Novel Bayes Model: Hidden Naive Bayes , 2009, IEEE Transactions on Knowledge and Data Engineering.

[21]  Salvatore Cuomo,et al.  A Cultural Heritage Case Study of Visitor Experiences Shared on a Social Network , 2015, 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC).

[22]  Shreyash Tambe,et al.  EFFECTIVE DATA MINING USING NEURAL NETWORKS , 2016 .

[23]  Younghwan Yoo,et al.  User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm , 2015, Sensors.

[24]  Bf Buxton,et al.  An introduction to support vector machines for data mining , 2001 .

[25]  Weifeng Chen,et al.  Advanced Service Technology and Application in Smart Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[26]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[27]  Xin Hou,et al.  A New Pedestrian Detect Method in Crowded Scenes , 2013, GreenCom/iThings/CPScom.

[28]  Li Xiu,et al.  Application of data mining techniques in customer relationship management: A literature review and classification , 2009, Expert Syst. Appl..

[29]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.