Clustering Methods in Data Mining

The process of grouping similar objects into clusters based on their characteristics.

A type of machine learning where the algorithm learns patterns without being explicitly told what to look for. It discovers hidden structures in data.

Measures how similar or different two data points are. It helps determine how closely objects should be grouped.

A method for analyzing and understanding data by grouping similar objects together. It aims to minimize the distance between objects within a cluster and maximize the distance between different clusters.

The average distance between all points within a single cluster. It should be minimized for effective clustering.

The average distance between points in different clusters. It should be maximized for effective clustering.

A distance metric where the distance between two points is calculated as the sum of the absolute differences of their coordinates.

A distance metric that uses the cosine of the angle between two vectors to measure their similarity. It's often used for text analysis.

A distance metric that calculates the straight line distance between two points in a multi-dimensional space.

Grouping documents with similar topics or themes together for easier browsing and analysis.

Identifying groups of similar items based on their characteristics. For example, grouping customers with similar purchasing patterns.

Clustering algorithms should be able to handle different types of data, like numerical, categorical, or binary.

Clustering results should be consistent regardless of the order in which the data is input.

Algorithms should be able to identify clusters with different shapes, not just round ones.

A desirable feature for clustering algorithms is to be able to handle noisy data.

Algorithms should be fast enough to work well even with large datasets.

The results of clustering should be easily understandable and useful for decision making.

Ability to pause and restart the clustering process for large datasets to avoid long waiting times.

A type of clustering where the data is divided into non-overlapping groups based on a predefined number of clusters (k). Each data point belongs to only one cluster. The goal is to optimize an objective function, often minimizing the distance between data points and their cluster centers.

Creates a hierarchy of clusters represented as a tree-like structure (dendrogram). This structure can be built either bottom-up (agglomerative) by merging clusters or top-down (divisive) by splitting clusters. This approach doesn't require defining the number of clusters beforehand.

One of the examples used in partition-based clustering. This algorithm groups data into k clusters around centroids. The centroids are iteratively updated until convergence is reached. This means the position of the centroids is adjusted until the data points assigned to each cluster are as close as possible to the centroid.

Similar to K-Means, but instead of using centroids, it uses actual data points (medoids) as cluster centers. These medoids are the most representative data points of their respective clusters.

A technique used in hierarchical clustering where the clusters are merged based on the closest pair of points. This means it considers the distance between the points closest to each other in two clusters, regardless of other points in the clusters.

A grouping of data points where each point is closer to every other point within the cluster than to any point outside of it.

Clusters formed by grouping data points that are closer to a central point or "prototype" of the cluster than to the prototypes of other clusters.

Clusters where each point is closer to at least one other point within the cluster than to any point outside of it.

Clusters identified by areas of high density, separated by areas of low density. Effective for clusters with irregular shapes and handling noise.

Clusters defined by optimizing an objective function. The 'goodness' of a clustering is evaluated based on the function.

A group of data points that have been clustered together.

Methods used to identify clusters with different shapes and sizes.

Assigns data points to clusters based on their probabilities of belonging to each cluster, allowing for overlapping memberships. It's useful for situations with uncertainty or fuzzy boundaries.

Incorporates specific rules or constraints into the clustering process, ensuring that the resulting clusters meet predefined criteria. It's beneficial when domain knowledge is available.

Utilizes the spectral properties of a similarity matrix to identify clusters. It's effective for handling non-linearly separable data.

Creates a hierarchical structure of clusters, starting from individual points and merging them into larger groups based on similarity. It reveals the relationships between clusters at different levels.

Identifies clusters based on the density of data points. It groups together points that are in high-density regions, separating them from low-density areas.

Focuses on the distribution patterns within the data and uses statistical models to form clusters. It's well-suited for datasets with inherent probability distributions.

A measure of how similar a point is to its own cluster compared to other clusters, ranging from -1 (poor clustering) to 1 (perfect clustering). A higher value indicates that the data point is well-assigned to its cluster.

A measure of the distance between clusters, calculated by dividing the minimum inter-cluster distance by the maximum intra-cluster distance. A higher Dunn index means that clusters are well-separated.

A technique that uses the within-cluster dispersion to determine the optimal number of clusters (k) in K-Means clustering. Visualized by plotting the within-cluster dispersion versus different k values. The optimal k value is often identified at the 'elbow' of the plot, marking the point where the decrease in dispersion slows down.

A technique that compares the within-cluster dispersion of your data with a reference dataset to find the optimal number of clusters (k). It calculates the gap statistic based on the difference between the expected and observed within-cluster dispersion.

The type of similarity or dissimilarity measure used in clustering algorithms, determining how close or far apart objects are based on features. Different measures are suitable for different data types and applications.

Data with many attributes, potentially leading to challenges in clustering. High dimensionality can create sparse data with many zero values, potentially making it difficult to find patterns.

Information that is irrelevant or erroneous, obscuring the true patterns in data. This can distort the results of clustering, leading to incorrect groups.

Points that are significantly different from the rest of the data points, potentially pulling clusters apart or creating false clusters. They can mislead clustering algorithms and negatively affect the results