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An Overview on Clustering Methods
T. Soni Madhulatha
TL;DR
Clustering groups similar data objects using algorithm-specific distance, linkage, or density criteria, but selecting and interpreting clusters remains challenging. This paper surveys hierarchical, partitional, density-based, and model-based approaches, along with cluster-count selection and comparison factors. It concludes that clustering is descriptive and its results depend on analyst choices and the variables used.
Problem
Clustering seeks structure in unlabeled data, but determining an appropriate number of clusters is itself a separate problem requiring dedicated techniques.
Method
The paper surveys clustering algorithms, including hierarchical, partitional, density-based, grid-based, and model-based methods, and describes distance measures and cluster-count criteria.
Results
The paper compares clustering algorithms using dataset size, number of clusters, dataset type, and software type.
Takeaways & Limitations
Clustering results are descriptive, depend strongly on analyst choices, and should not be generalized beyond the dimensions or variables used.
Takeaways & Limitations
Clustering always produces groups even when no group structure exists, and cases within a cluster are similar only with respect to the analyzed variables.
Abstract
from arXiv · showhide
Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data in each subset according to some defined distance measure. This paper covers about clustering algorithms, benefits and its applications. Paper concludes by discussing some limitations.
I. INTRODUCTION
Clustering is an unsupervised learning task that seeks structure in unlabeled data by grouping similar objects and separating dissimilar ones. The paper distinguishes hierarchical and partitional algorithms.
- Clustering groups similar objects together while separating objects that are dissimilar.
- As an unsupervised learning problem, clustering searches for structure in collections of unlabeled data.
- Hierarchical algorithms build successive clusters from established clusters, whereas partitional algorithms determine all clusters at once.
- Hierarchical clustering may be agglomerative, merging individual clusters, or divisive, repeatedly splitting the whole set.
HIERARCHICAL CLUSTERING
Hierarchical clustering organizes data through successive merges or splits, with distance measures and linkage choices determining how clusters are formed. The section also discusses partitional, density-based, and related clustering methods, along with their computational properties and limitations.
- HIERARCHICAL CLUSTERING: Hierarchical clustering requires a distance measure, such as Manhattan or Euclidean distance, to assess similarity between objects.Manhattan distance sums absolute differences; Euclidean distance uses the square root of summed squared differences.
- HIERARCHICAL CLUSTERING: Agglomerative clustering starts with singleton clusters and progressively merges the closest elements, optionally using a distance matrix.
- HIERARCHICAL CLUSTERING: Linkage choices include maximum, minimum, and mean intercluster distance, known respectively as complete, single, and average linkage.
- HIERARCHICAL CLUSTERING: Agglomerative merges occur at increasingly greater distances, and clustering can stop when clusters are too far apart or sufficiently few.
- HIERARCHICAL CLUSTERING: Divisive clustering starts with all documents in one cluster and recursively splits them until each document forms a singleton cluster.
- HIERARCHICAL CLUSTERING: Hierarchical methods cannot undo completed merges or splits, reducing computation costs but making the procedure rigid.
- HIERARCHICAL CLUSTERING: K-means assigns points to the nearest centroid, while k-medoids represents clusters with central objects and is more robust to noise and outliers.
- HIERARCHICAL CLUSTERING: DBSCAN identifies dense regions and can handle large datasets, noise, and clusters with different sizes and shapes.
SNN ALGORITHM
SNN clustering defines similarity through shared nearest neighbors and uses this measure to identify dense core regions and noise. The section also covers parameterization, cluster-count selection, grid-based methods, and model-based approaches.
- SNN ALGORITHM: SNN defines similarity between points by counting shared nearest neighbors, then uses neighbor similarities to estimate local density.
- SNN ALGORITHM: High-density points become core points, while low-density points are treated as noise; strongly similar points join core-centered clusters.
- SNN ALGORITHM: SNN requires parameters for neighbor-list size, density threshold Eps, and the MinPts threshold defining core points.
- SNN ALGORITHM: Grid-based clustering quantizes space into cells, removes cells below a density threshold, and forms clusters from adjacent dense cells.
- SNN ALGORITHM: Grid-based boundaries are horizontal or vertical, so diagonal boundaries are not detected.
- SNN ALGORITHM: Model-based clustering fits data to mathematical models, often assuming generation from mixtures of probability distributions.
- SNN ALGORITHM: The elbow criterion selects a cluster count near the point where adding another cluster produces only a small marginal gain in explained variance.
IV. HOW ALGORITHMS ARE COMPARED
The paper compares clustering algorithms using dataset size, number of clusters, dataset type, and software type. It presents a table as the basis for the comparison and its conclusions.
- The algorithms are compared by dataset size, number of clusters, dataset type, and software type.
- Table 1 explains how the four algorithms are compared and provides the stated conclusions.
V. POSSIBLE APPLICATIONS
Clustering is applied across diverse domains to identify groups based on shared characteristics, behaviors, risks, or spatial patterns.
- Marketing uses clustering to find customer groups with similar behavior from properties and past buying records.
- Financial applications include forecasting markets and exchange rates, assessing bankruptcies and risk, trading futures, and credit rating.
- Biology applies clustering to classify plants and animals according to their features.
- Insurance uses clustering to identify high-cost motor policyholder groups and detect fraud.
- City planning groups houses by type, value, and geographic location, while earthquake studies cluster epicenters to identify dangerous zones.
- Web applications include document classification and clustering web-log data to discover groups with similar access patterns.
VI. CONCLUSION
The conclusion emphasizes that clustering is descriptive and dependent on analyst choices, so its results require cautious interpretation and should not be generalized.
- Clustering is descriptive, and its solution depends strongly on the analyst’s choices.
- Combining different clustering results can produce more stable clusters that depend less on the selected analysis criteria.
- Clustering always produces groups, even when no genuine group structure exists, because the analysis hypothesizes that groups are present.
- Results should not be generalized because within-cluster similarity applies only to the variables used to induce dissimilarities.