Why is Data Mining Needed?
Every organization wants to maximize its
revenues by providing a competitive advantage over other companies. A strategy
that offers high-quality products to end-users and gives them a delightful user
experience can lead to increased sales volume. However, traditional strategies
of business and marketing leaders focus on only one aspect of success — selling
products and services. They overlook the needs of the whole customer base,
focusing mainly on satisfying specific needs that they had identified
previously. The result is low profits. There is a need for understanding why
and when data mining would help companies to reach their goals.
Let me tell you another story. We were running
an eCommerce website. Our target audience is young adults between 15–42 years
old. As a part of our work team, we regularly got requests for collecting
demographic data such as age and sex. But before getting started, we needed to
decide if data mining was the best approach to work with our data. You see,
data mining is not limited to age and sex data. Any person, industry, or
country would need the same data set to get the desired insights. So, data
mining would need to include both quantitative and qualitative data sources.
Let’s explore some examples of successful data
mining, below are some common questions and data mining techniques being used
today. Unsupervised machine learning was probably one of the most highly used
data mining tools in data science today. If you are interested in how data
mining and AI successfully meet each other, read How to Do Deep Learning
And Artificial Intelligence Share Similarity Measures, where Ian Good fellow,
Yoshua Bengio details their similarities.
K-Means Clustering: Data Science Tool
K-Means clustering is one of the simplest data
mining methods. It assumes that there are underlying structures present in your
dataset and then divides your dataset into 2 clusters. One of the main
advantages of this technique is the ease of model building. All the observations
are assumed to belong to one cluster and then, similar observations of other
clusters are assigned to that particular cluster with a probability
(represented by k). Thus, we can get insight into the data from having K
clusters for different values of k.
K-Means clustering requires no programming
knowledge required, just basic familiarity with statistics or linear algebra.
Furthermore, it also makes it possible to build complex models quickly with
less computational data. With K-Means clustering we can extract value from the
attributes of a dataset and use these features to predict related attributes.
Thus, without any data engineering, which is almost impossible in other statistical
methods. Using K-Means clustering in a data scientist role provides him/her
with several benefits including but not limited to:
Cluster structure analysis.
Extracting value from attribute data. These
values can be converted into numerical values and visualized through a heat map.
Evaluation of fit and prediction errors for
models.
Understanding of individual clusters’
characteristics.
Discovery of clusters with minimal cases and
clusters with the highest number of instances.
K-Means Clustering is great for simple
datasets and it is suitable for modeling and exploratory analysis of larger
data sets. However, K-Means would be much harder to understand and execute than
clustering. This is because of the small number of dimensions (dimensions) in
data, which is used to define the clusters. Therefore, K-Means makes it
difficult to do exploratory analysis and model building.
Another advantage of K-Means clustering is the
ability to easily detect and separate clusters. Hence, when applied in
production or manufacturing settings, K-Means is quite appropriate to find the
optimal output and process.
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