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Running head: CLUSTER ANALYSIS

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Data mining using cluster analysis
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CLUSTER ANALYSIS

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Data mining using cluster analysis

Today's digital world has a vast amount of data envisioned to grow into the next decade
by 40%; despite being surrounded by this big data, most people are starving for knowledge. This
data can be extracted to gain useful information and understanding that help make important
decisions through data mining. Data mining involves discovering patterns that exist in these
extensive data. There are several techniques used to extract data, depending on the nature of the
information required. One of these techniques is cluster analysis that mostly uses k- means
algorithm to partition data into different clusters, which in my opinion, is the best way when
collecting data from customer surveys, customer reviews, and social media opinions.
Cluster analysis examines data and puts them in different groups where data with similar
characteristics are placed in one group (Anderberg, 2014). They differ with data in the other
group using algorithms. When it comes to collecting survey data from the customer and their
opinions on social media, I believe it is the best because it categorizes the findings according to
their characteristics. After grouping the data, it is easier to know where most people's opinions
lie on the negative or positive sides. It also helps in identifying define patters between the data
collected. Cluster analysis is modified to work best even if some changes are made primarily on
people's comments.
Furthermore, its algorithm has some...