medoid
Sign in to saveMedoids are representative objects of a data set or a cluster within a data set whose sum of dissimilarities to all the objects in the cluster is minimal. Medoids are similar in concept to means or centroids, but medoids are always restricted to be members of the data set. Medoids are most commonly used on data when a mean or centroid cannot be defined, such as graphs. They are also used in contexts where the centroid is not representative of the dataset like in images, 3-D trajectories and gene expression (where while the data is sparse the medoid need not be). These are also of interest whil
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Article
25 sectionsContents
- Definition
- Clustering with medoids
- Algorithms to compute the medoid of a set
- Implementations
- Medoids in text and natural language processing (NLP)
- Text clustering
- Text summarization
- Sentiment analysis
- Topic modeling
- Techniques for measuring text similarity in medoid-based clustering
- Cosine similarity
- Jaccard similarity
- Euclidean distance
- Edit distance
- Medoid applications in large language models
- Medoids for analyzing large language model embeddings
- Medoids for data selection and active learning
- Medoids for model interpretability and safety
- Real-world applications
- Gene expression analysis
- Social network analysis
- Market segmentation
- Anomaly detection
- References
- External links
Medoids are representative objects of a data set or a cluster within a data set whose sum of dissimilarities to all the objects in the cluster is minimal. Medoids are similar in concept to means or centroids, but medoids are always restricted to be members of the data set. Medoids are most commonly used on data when a mean or centroid cannot be defined, such as graphs. They are also used in contexts where the centroid is not representative of the dataset like in images, 3-D trajectories and gene expression (where while the data is sparse the medoid need not be). These are also of interest while wanting to find a representative using some distance other than squared euclidean distance (for instance in movie-ratings).
For some data sets there may be more than one medoid, as with medians. A common application of the medoid is the k-medoids clustering algorithm, which is similar to the k-means algorithm but works when a mean or centroid is not definable. This algorithm basically works as follows. First, a set of medoids is chosen at random. Second, the distances to the other points are computed. Third, data are clustered according to the medoid they are most similar to. Fourth, the medoid set is optimized via an iterative process.