Unsupervised Learning in Data Analysis: Grouping Objects and Data

This essay delves into the application of unsupervised learning techniques in data analysis, focusing on clustering methods for grouping objects and data within unlabelled datasets. It highlights the use of clustering to isolate data into multiple groups based on attribute similarity, emphasizing the roles of dimensionality reduction and exploratory analysis. The essay contrasts unsupervised learning with its supervised counterpart, noting its inherent complexity and subjectivity due to the absence of predefined goals. It elaborates on three primary clustering types: K-Means Clustering, Hierarchical Clustering, and Probabilistic Clustering, detailing their methodologies and benefits. Furthermore, it discusses the advantages and application areas of unsupervised techniques, particularly in customer segmentation, where algorithms identify inherent similarities between datasets to create new labels for distinct groups, aiding organizations in refining their understanding of customer demographics and targeted advertising strategies. The essay concludes by referencing relevant research in the field, underscoring the practical implications of unsupervised learning in data-driven decision-making.

Running head: UNSUPERVISED LEARNING IN DATA ANALYSIS
Unsupervised Learning in data analysis
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1UNSUPERVISED LEARNING IN DATA ANALYSIS
Unsupervised techniques for grouping objects or data in data
analysis
In data analysis, unsupervised learning is helpful in exploring the hidden patterns in
a given unlabelled dataset. One of the best examples of unsupervised learning is clustering
technique. The clustering is defined as the process of isolating the data in multiple groups in
which the data points are close to each other from the certain attribute point of view. In
addition to that data points of diverse groups are not similar. The main two usage of the
unsupervised learning are dimensionality reduction and exploratory analysis. Compared to
the supervised learning, Unsupervised learning is extensively complex and subjective (Yang,
Parikh & Batra, 2016). The reason behind this can be stated as there is no predefined goal for
carrying out the analysis as in case of supervised learning (prediction of a response depending
on the patterns).
There are mainly three types of clustering used in unsupervised learning which are K-Means
Clustering, Hierarchical Clustering, Probabilistic Clustering.
In case of clustering, the idea of dimension reduction is used. In this way after the clustering
it is possible to represent the selected data through the use of lesser number of columns/
features (through unsupervised way).
K-Means Clustering: In this clustering method the data points are segregated in finite
number of reciprocally exclusive clusters. In this method the amount of complexity is
considered as high due to the determination of criteria for the right number K.
Hierarchical Clustering: In this clustering method the selected dataset is divide in
parent, child clusters. This helps in splitting the customer dataset among younger and older
time period. After this, the child nodes can be again divided in their own individual clusters

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according to a well-defined criterion. This Method is relatively simple under unsupervised
techniques of data analysis. In this method the data set is iteratively divided and a tree like
structure is created with the increasingly small sized clusters. In this approach the Euclidean
distance among the scaled variables are measured (Celebi & Aydin ,2016). In this iterative
manner the previously considered dataset is divided into clusters as individual distance values
are overlapped. At the end the, form the tree structure any segment can be from any height
can be captured with finite number of clusters.
Probabilistic Clustering: In this clustering method the data points are divided into clusters
depending on the probabilistic scale.
Benefits and application area of unsupervised technique
In unsupervised technique the algorithms considered the inherent similarities between
the different data sets. The algorithm separates the data in groups accordingly by assigning
new labels for each group derived from the dataset. This kind of algorithm is beneficial for
customer segmentation processes as it returns clusters based on some specific parameters
which cannot be determined before the analysis (Yang, Parikh & Batra, 2016). In this way
organisations can clearly define the demographics for their products or services. In this way,
it is possible by identify different subgroups from the dataset in order to find out the more
receptive customer base for targeting the particular form of advertising.

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References
Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018). Deep clustering for unsupervised
learning of visual features. In Proceedings of the European Conference on Computer
Vision (ECCV) (pp. 132-149).
Celebi, M. E., & Aydin, K. (Eds.). (2016). Unsupervised learning algorithms (Vol. 9, p. 103).
Springer.
Hu, F., Xia, G. S., Wang, Z., Huang, X., Zhang, L., & Sun, H. (2015). Unsupervised feature
learning via spectral clustering of multidimensional patches for remotely sensed scene
classification. IEEE Journal of Selected Topics in Applied Earth Observations and
Remote Sensing, 8(5).
Yang, J., Parikh, D., & Batra, D. (2016). Joint unsupervised learning of deep representations
and image clusters. In Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (pp. 5147-5156).