Skip to content

JA3. Principles of Data Clustering

Statement

  • In this learning journal, explain in detail three basic principles of data clustering.

Answer

Introduction

Data analysis is the process of digesting raw data into useful information; it forms a framework that involves defining objects, planning, collecting data, processing and analyzing data and lastly, interpreting the results. Clustering is a technique that is used in processing and analyzing data step.

Clustering is a process that involves grouping data points (objects) into similar groups for further analysis. Principles of Clustering are the foundational concepts and goals behind the practice of clustering. Clustering Methods are specific algorithms or techniques used to perform clustering based on the principles; there are more than 100 clustering algorithms available (Nvidia, 2021).

This text will discuss three basic principles of data clustering: Similarity, Dimensionality, and Interpretability.

Similarity

The clustering process will try to classify objects into groups based on their similarity; similarity usually means the distance between the two values of a property from the object and the reference object. Due to the nature of the process, all data must be converted into numerical values before clustering.

The similarity measure can be calculated using different methods such as Euclidean, Manhattan, Minkowski, and Chebyshev distance (Soler et al, n.d., p.2). The choice of similarity measure depends on the nature of the data and the clustering method used.

The reference object varies depending on the clustering method used; it may be the centroid of a predefined cluster, a randomly selected object, just the closest object to the object being clustered, or the centroid of another cluster. The similarity measure can answer questions such as whether an object belongs to a specific cluster or how similar is an object to another object.

The values need to be normalized or standardized properly before calculating the distance; this has the benefit of limiting the minimum and maximum values of the results and simplifying the computation and comparison processes. There is also the issue of outliers which should be handled, and missing values should be assumed as a suitable value or removed from the dataset (Akalin, 2020).

Dimensionality

Dimensionality is the number of attributes that describe objects and are being observed during the clustering process; High-dimensional data means that we care about too many properties at once while doing the clustering which requires more computational power and time as most of the space is empty and the objects are sparse (Steinbach et al, n.d., p.12).

The lower the dimensionality, the easier the process of clustering becomes; thus, the process of handling high-dimensional data involves lowering its dimensionality but the accuracy of results may go down due to the loss of information. There are a few techniques for lowering the dimensionality: first, feature reduction, where we simply omit some properties from the clustering; second, feature extraction, where we combine some properties into a single new property that becomes the focus of the clustering; and third, feature selection, where we select a subset of properties that are most important for the clustering (Margel & Shtar, 2017).

Interpretability

The results of clustering should be meaningful and understandable; depending on the objectives of the analysis, the size of the problems, and other context factors, the results may be classified as interpretable or not.

The interpretability should be discussed thoroughly during the planning processes, and before doing the actual analysis, and the consumers of the results should be taken into account and surveyed to know their expectations and needs. High dimensionality also reduces interpretability as humans can only comprehend up to a few dimensions ( Alvarez-Garcia et al, 2024).

The analysis is as good as the interpreters of the results can make use of it to make the right decisions; so such a factor should not be ignored while doing the clustering process.

Conclusion

The text discussed three basic principles of data clustering: Similarity, Dimensionality, and Interpretability. Similarity is the process of grouping objects that are similar to each other based on some criteria. Dimensionality is the process of managing high-dimensional data without significant loss of information. Interpretability is the process of making the results of clustering meaningful and understandable.

References

‌

‌ ‌ ‌

‌ ‌ ‌