2. Introduction
Data mining- the practice of extracting vital information from larger data sets.
It allows the analyst to make sense of the data, and possibly deduce key patterns from the
provided information (Chen et al., 2015 p.1).
The internet of things refers to the interconnection of computer systems, in such a way that
data can be passed from one machine to the next.
From the article “Data Mining for the Internet of Things: Literature Review and
Challenges” by Chen et al., the Internet of Things (IoT) is considered as a vital technology
which obtained popularity since its invention.
3. Steps of Data Mining
• Data preparation- This step is further divided into three sub steps which include (Tan, 2018) ;
1. Merging data from different sources
2. Data cleaning- done to eliminate mistakes and errors in the data.
3. Selection of relevant data.
• Data Mining- involves extracting different sets of data from the larger data set.
• Data Presentation- this step allows us to have a visual insight into the data by means of graphs and
scatter plots.
4. Data Mining Functionalities
Major functions of data mining include;
a) Classification- it involves the grouping of data into cognizable groups (Tsai et al.,
2013 p. 85).
b) Clustering- it assigns categories to data based on their similarities and differences
(Amani and Fadlalla, 2017 p.40).
c) Associations analysis- reveals the different associations that exist between
variables.
5. Applications of Data Mining
Data mining is used widely in several fields.
1. Data mining in healthcare- its used for prediction of patients survival rates and to predict
the effectiveness of interventions.
2. Data mining in e-commerce- it can be used to reveal underlying patterns and trends, which
help boost the performance of the business (Sethi and Verma, 2016 p.586).
3. Data mining in marketing- by indicating the most effective strategies, data mining guides
the process of decision making in many business worldwide.
6. Challenges Facing Data Mining and IoT
The most common challenges associated with the Internet of things and Data mining
include the following;
1) Large sets of data- data mining requires the use of large data sets, which may be
challenging to correct existing errors, and may require more resources (Chen et al., 2015
p.7).
2) Need for high technical knowledge- analysts are required to be adequately conversant with
the various tools used for data mining (Bin and Xiaoyi, 2010 p.130). This may force
organizations to organize for trainings or outsourcing of data miners.
3) Wide variety of plausible data sources- this leaves the potential of using aboriginal sources,
or those that are below standards, which may further predispose to error.
7. References
Data Mining 1
Amani, F.A. and Fadlalla, A.M., 2017. Data mining applications in accounting: A review of the literature and organizing
framework. International Journal of Accounting Information Systems, 24, pp.32-58.
Bin, S., Yuan, L. and Xiaoyi, W., 2010, April. Research on data mining models for the internet of things. In 2010 International
Conference on Image Analysis and Signal Processing (pp. 127-132). IEEE.
Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A.V. and Rong, X., 2015. Data mining for the internet of things: literature review
and challenges. International Journal of Distributed Sensor Networks, 11(8), p.431047.
Sethi, S., Malhotra, D. and Verma, N., 2016. Data mining: current applications & trends. International Journal of Innovations in
Engineering and Technology, 6(4), pp.586-589.
Tsai, C.W., Lai, C.F., Chiang, M.C. and Yang, L.T., 2013. Data mining for internet of things: A survey. IEEE Communications
Surveys & Tutorials, 16(1), pp.77-97.
Tan, P.N., 2018. Introduction to data mining. Pearson Education India.
Feng, Z. and Zhu, Y., 2016. A survey on trajectory data mining: Techniques and applications. IEEE Access, 4, pp.2056-2067.
