A distribution-based model that summarizes a multivariate probability density function with a mixture of Gaussian distributions. More flexible than k-means.
GMM is a distribution-based soft clustering model that assumes data is composed of a mixture of probabilistic distributions, typically Gaussian distributions. It is noted for providing flexibility that traditional centroid-based methods (like K-Means) lack. GMM is utilized for tasks such as image segmentation and analysis of customer behavior.
Sources & References
Explore other ML Models & Algorithms terms
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BERT (Bidirectional Encoder Representations from Transformers)
The foundational language model used for transformer-based embeddings in BERTopic.
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BERTopic
An unsupervised machine learning approach for topic modeling that generates interpretable topics and performs dynamic…
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BERTopic
An unsupervised machine learning approach for topic modeling that generates interpretable topics and performs dynamic…
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BIRCH (Balanced Iterative Hierarchical Based Clustering)
A hierarchical clustering method efficient for large datasets and time series.
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Boyer-Moore
An exact string-matching algorithm and one of the best-known pattern recognition algorithms.
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c-TF-IDF
Class-based Term Frequency-Inverse Document Frequency; used by BERTopic for clearer topic representation and selection of…
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DBSCAN
Density-Based Spatial Clustering of Applications with Noise; groups data points based on density. Useful for…
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Decision Tree
An early, simple model for classification or regression.
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Distance-based matching
Fuzzy matching methods focusing on "edit distance" rather than exact spelling.
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DistilBERT (Refined Query Semantic Class Classifier)
A fine-tuned BERT model used for semantic class classification based on queries.
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Encoder Model
A machine learning model used in Google's two-step process for building and maintaining the Knowledge…
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Fuzzy Matching / Fuzzy String Matching
A string similarity assessment approach, typically relying on character distance rather than semantics, used to…