Clustering Techniques: Grouping Similar Data Points

Clustering is a critical technique often used in machine learning and data analysis. It involves grouping similar data points depending on their characteristics. It has a vital role in delivering insights from complex datasets, identifying a pattern between them, and a deeper understanding of the data points.

There are several clustering techniques available, each unique, and can be used according to the type of data, assumptions made, and the required objective. 

To pursue a career in Data Analytics, read this blog to learn more about clustering techniques, how they are individually different, and their real-life uses. 

Objectives of clustering techniques

The main objectives of these algorithms are:

1. It aims to create clusters or groups based on inherent characteristics of the data points that are similar to each other in some aspects. 
2. It helps to identify concealed patterns or relationships within the data structures that may not be immediately apparent. 
3. It allows for the identification of data distribution, distinguishing outliers, and gaining insights into the various characteristics of the dataset. 
4. It reduces the complexity of large datasets by reducing them to smaller clusters which can be easily put to the test and further used for decision-making and problem-solving. 

Advantages and disadvantages of clustering techniques

The major advantages of following these techniques are:

1. It helps discover insights using data points that can be useful for decision-making. 
2. It is an unsupervised technique making it flexible and easy to be used in various domains. 
3. It provides a visual representation of the dataset by grouping similar points which leads to a better understanding of the data. 
4. It helps in detecting the outliers in the data and often makes them stand out during clustering for better identification. 
5. This is widely used for customer segmentation which is used by businesses to understand the customer base, their needs, and preferences. 

These also come with certain disadvantages which are as follows:

1. Clustering may require several parameters that may be subjective and hence impact the quality of the results. 
2. Complex trials are to be run with different initialisations to assess the reliability of results. 
3. These techniques often face multiple challenges when dealing with high-dimensional data. 
4. It lacks accuracy as there is no ground truth to compare the results against. 
5. It becomes challenging to apply these techniques to high-dimensional data without proper resources. 

How to choose the clustering technique

Here are some important points to consider when selecting a clustering technique:

1. Data characteristic- Consider the nature of the data like dimension, distribution, and the presence of outliers. 
2. Scalability- Try to understand the scalability of the dataset from its size and the resources available. 
3. Interpretability- Assess the interpretability of the resulting clustering and the insights provided by it.
4. Assumptions- Ensure that the assumptions made by various clusters align with the data. 
5. Performance- Consider the robustness of the technique to the outliers in the data and choose accordingly. 
6. Experimenting- Try different clustering techniques together and compare the results. It will help in a comprehensive understanding of the information. 
   

Different clustering techniques

Here are some commonly used clustering techniques:

K-Means clustering

It is one of the most used clustering algorithms. Python programming is commonly used for this technique due to its extensive database support and flexibility in handling huge datasets. It divides the data into k clusters, where k is already defined, and assigns data points to the nearest centroids and recalculates centroids, until convergence. It is computationally efficient and assumes that clusters are spherical and equal in size. 

Hierarchical clustering 

It creates a hierarchy of clusters by splitting and merging the existing ones, without specifying the number. It can be divided into two further approaches- agglomerative and divisive. It captures complex relationships and provides a dendrogram representation of the clustering structure. It uses data visualisation tools like Power BI for interactive visualisations and to explore and analyse the clustered data easily. 

Gaussian Mixture Models (GMM)

It assumes that data points are generated from Gaussian distributions. It models clusters using means and covariances. It assigns data points to various clusters depending on the probability of belonging to each distribution. This clustering is flexible and can capture clusters with different orientations. 

Spectral clustering

It uses graph theory and linear algebra to group data based on similarity creating a matrix and transforming it into lower dimensional space using eigenvector decomposition. It effectively captures non-convex shapes. 

OPTICS (Ordering Points To Identify the Clustering Structure)

It is a density-based clustering technique that orders data points based on reachability, identifying clusters of varying density and capturing the data’s structure without specifying cluster numbers. 

Mean Shift clustering

It is a non-parametric clustering technique that iteratively shifts data points toward higher-density regions. It starts with a kernel density estimator which computes the mean shift vector pointing towards the highest density gradient. This process continues till convergence and finally forms clusters. Due to its complexity, it may struggle with large datasets. 

Affinity Propagation 

It is a message-passing technique that uses data points as network nodes to balance efficiency and accuracy. Iterative exchanges of messages reflect similarity and availability but cluster size can be sensitive to input parameters. 

Density-Based Spatial Clustering of Applications with Noise

It is a density-based clustering algorithm that groups dense data points and separates sparse ones. It uses epsilon and minimum points to identify core points and expand clusters. It is effective in detecting clusters of arbitrary shapes and can handle noisy data.  

Conclusion

Clustering techniques help in data analysis, revealing data patterns and structures. Selecting the right technique improves results and decision-making. Imarticus Learning’s Postgraduate Program in Data Science and Analytics is an industry-oriented data science course with placement opportunities. This program focuses on practical applications of statistical analysis, machine learning certification, data visualisation courses, and big data technologies.

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