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Peer responses:
Discussion 1:
Introduction
The algorithms that are used for cluster analysis need to be decided based on the
requirements of the practice. It is the skills and intelligence of the professionals that would be
required to support the analysis with an accurate algorithm. Data and its nature also influence the
need for an algorithm.
K-means from a basic standpoint
It is unlabelled data that gets grouped after assessing the traits within it. One gets to
assemble similar items and team them up so as to support the identifications. It is observations
that get derived using the K-means that further enable effective clustering of the materials. Not
involving defined groups makes the data appear to be unlabelled and can be arranged and
segmented using the K-means. It reflects the traits of unsupervised learning that get used for
unlabelled information. Data contains groups that need to be detected and analyzed for further
usage. Using K as the variable, the groups get represented, which is one of the goals of such an
algorithm. It is a simple tool that can be used for a better understanding of the items. Huge
datasets get easily managed using it, which is a scalable and efficient technique. ‘Medical
practitioners utilize the K-means to assess the severity of asthma patients’ (Birs et al., 2023).
Various types of clusters
Centroid-based clustering is one of the types that relies on arranging the data into
clusters that are non-hierarchical. The k-means is one of the algorithms that fall under the
centroid-based structure. The density-based clustering would be another sort that detects
distinctive clusters. It reflects the machine learning technique in unsupervised form. The data
sets get analyzed based on the points and their local density. The spatial datasets can be assessed
using such type. Distribution-based clustering organizes the points based on measuring the
likelihood of relating to the arrangement of the previous data distribution probability. The
process of clustering chooses the statistical distribution technique for attempting the clustering
practice.
Strengths and weaknesses of K-means
K-means includes several such strengths and weaknesses that need to be assessed for
better realizing the algorithm. A professional can process it better after such realizations. Some
of the advantages would be its simplicity at implementation, efficiency for large data scaling,
assures convergence, adaptability, centroid positions received a warm-start and easy discovery of
cluster shapes and sizes that can be processed. On the other hand, the disadvantages would be
initial condition sensitivity, determining challenges, time complexity, and incompetence with
categorical data.
Cluster evaluation
Evaluating the clusters makes one process of predictive analysis effective. It is
conducted to recognize the intra-cluster resemblance that is high along with limited inter-cluster
similarity. To analyze the attributes of clustering, such practices reflect the internal
criterion. ‘Effective evaluations can be processed when every type gets combined to perform
such acts’ (Cantner et al., 2019). Hard and soft clustering are the two types under cluster
evaluation. Data point in hard clustering highlights whether it is associated with the cluster or
not. However, data point within soft clustering connects itself to clusters more than a single one.
Soft clustering involves an example, which is Fuzzy C-means.
Conclusion
The groups that emerge naturally within data require identification that can be practiced
using the clustering technique.
References
Birs, I., Boulay, M., Bertrand, M., Côté, A., & Boulet, L. (2023). Heterogeneity of asthma with
nasal polyposis phenotypes: A cluster analysis. Clinical and Experimental
Allergy, 53(1), 52–64. https://doi.org/10.1111/cea.14247
Cantner, U., Graf, H., & Rothgang, M. (2019). Geographical clustering and the evaluation of
cluster policies: introduction. The Journal of Technology Transfer, 44(6), 1665–
1672. https://doi.org/10.1007/s10961-018-9666-4
Discussion 2:
Question 1
K-means is a prototype-based clustering technique that uses one-level partitioning for
data objects. It uses mean to describe a prototype. This mean is the mean of a group of points
applied in a defined space. K is usually a user-defined parameter signifying the number of
desired clusters. During its work, each point is assigned to the closest centroid to form a cluster.
The centroids update depending on the points to the cluster. These updates continue until no
point changes clusters.
Question 2
Several clusters exist, such as well-separated clusters, prototype-based clusters, graphbased clusters, density-based clusters, and conceptual lusters (Portugal et al., 2020). Wellseparated clusters have each object closer to every other object in the cluster than the objects
outside the cluster. Prototype-based clusters have objects in a cluster closer to the defining
cluster than any other cluster’s prototype. Graph-based clusters have data represented in a graph
where a group of objects is connected to one another but not to any object outside the group.
Density-based clusters have regions of high density separated from the regions of low density.
Lastly, conceptual clusters contain objects that share some property.
Question 3
K-means is simple and applies to most data types. It is also efficient after multiple runs.
According to Tan et al. (2016), some K-means variants are more resistant to initialization
problems. However, K-means is not suitable for all data. For example, it finds pure subclusters
when there is a large number of clusters specified. Furthermore, it has challenges when there are
outliers in data. Still, it is effective for data with centroids.
Question 4
Cluster evaluation or validation is assessing the resulting classification model to
determine its accuracy or performance in its desired field of operation. It is a cross-checking step
that ensures the created model is effective in its operational area to verify the model’s
effectiveness.
References
Portugal, I., Alencar, P., & Cowan, D. (2020). A Framework for Spatial-Temporal Trajectory
Cluster Analysis Based on Dynamic Relationships. IEEE Access, 8, 169775-169793.
Tan, P. N., Steinbach, M., & Kumar, V. (2016). Introduction to data mining. Pearson Education
India.
