Clustering: explore techniques and methods

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Clustering

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Le clustering is an unsupervised machine learning technique that involves grouping similar objects or data points into distinct groups or classes.

Clustering algorithms make it possible to identify and highlight the underlying structures present in a data set, without the need for previously assigned labels to guide the model.

The objectives of clustering

The main aim of clustering is to divide a data set into groups with common characteristics, where each group is made up of a string of data with similar properties. This approach helps researchers and data analysts to obtain meaningful information about the distribution and general trends in the data. Practical applications of clustering include:

• Customer segmentation in marketing
• Classifying text documents
• Analysis of social networks
• Image and pattern recognition
• Recommendation systems

The different clustering methods

There are several clustering methods, some of which are better suited to certain types of problem than others. Here are some of the main methods used:

Hierarchical clustering

This method builds a hierarchy of clusters from a data set by progressively merging the closest groups. The agglomerative hierarchical clustering is a bottom-up approach, which starts with each piece of data as a separate cluster, then merges the closest pairs until only one cluster remains. Conversely, the divisive hierarchical clustering starts with a single group encompassing all the data and divides it successively into sub-groups.

Clustering by partitioning

Clustering by partitioning aims to divide a data set into a predetermined number of non-overlapping partitions. One of the best-known algorithms in this category is the K-meanswhich assigns each data point to a pre-defined centroid, so that the sum of the squared distances between each point and its centroid is minimised.

Density-based clustering

In this method, a cluster is considered to be a dense area of data points separated by less dense areas. The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an example of a density-based algorithm that can identify arbitrary cluster shapes, as well as detect and isolate noise points from the main cluster.

Model-based clustering

This method is based on the idea that data can be described by a number of statistical models. The Gaussian mixture clusteringfor example, assumes that each cluster follows a Gaussian distribution. Using the maximum likelihood method, the algorithm estimates the parameters that characterise each cluster and assigns to each data item the probability of belonging to each of the groups.

Similarity measurements and validation criteria

In order to determine the similarity between data points and carry out clustering, various distance measures can be applied:

• Euclidean distance
• Distance from Manhattan
• Distance from Chebychev
• Cosine similarity
• Pearson correlation

To assess the quality of a clustering result, we use internal or external validation metrics. Internal metrics assess the consistency of a set of clusters without recourse to external information, such as the Silhouette index or the within-cluster sum of squares. External metrics, on the other hand, compare the clustering results with an existing reference partition, such as the adjusted Rand index or purity.

Challenges and improvements

Despite their usefulness in many areas, clustering algorithms have certain limitations. Common challenges include

• Determining the optimum number of clusters
• Sensitivity to initialization and noise points
• Scalability for large data sets
• Detecting non-convex clusters or clusters of varying density

To overcome these challenges, various improvements and variants of the basic methods have been developed. For example, K-means++ provides more robust initialization, while MiniBatch K-means speeds up processing for large datasets.

In short, clustering is a versatile and relevant method for extracting information from a set of unlabelled data. Thanks to the diversity of approaches and algorithms available, it can be adapted to address complex problems in a wide range of application domains.