Different text mining methods are used based on their suitability for a data set. Text mining is the process of extracting data from unstructured text and finding patterns or relations. Below is a list of text mining methodologies.
Centroid-based Clustering: Unsupervised learning method. Clusters are determined based on data points.[1]
Fast Global KMeans: Made to accelerate Global KMeans.[2]
Global-K Means: Global K-means is an algorithm that begins with one cluster, and then divides in to multiple clusters based on the number required.[3]
KMeans: An algorithm that requires two parameters 1. K (a number of clusters) 2. Set of data.[4]
FW-KMeans: Used with vector space model. Uses the methodology of weight to decrease noise.[5]
Two-Level-KMeans: Regular KMeans algorithm takes place first. Clusters are then selected for subdivision into subclasses if they do not reach the threshold.[6]
Cluster Algorithm
Hierarchical Clustering
Agglomerative Clustering: Bottom-up approach. Each cluster is small and then aggregates together to form larger clusters.[7]
Divisive Clustering: Top-down approach. Large clusters are split in to smaller clusters.[8]
Density-based Clustering: A structure is determined by the density of data points.[9]
DBSCAN
Distribution-based Clustering: Clusters are formed based on mathematical methods from data.[10]
Expectation-maximization algorithm
Collocation
Stemming Algorithm
Truncating Methods: Removing the suffix or prefix of a word.
Lovins Stemmer: Removes longest suffix.
Porters Stemmer: Allows programmers to stem words based on their own criteria.
Statistical Methods: Statistical procedure is involved and typically results in affixes being removed.
N-Gram Stemmer: A set of 'n' characters that are consecutive taken from a word
Hidden Markov Model (HMM) Stemmer: Moves between states are based on probability functions.
Yet Another Suffix Stripper (YASS) Stemmer: Hierarchal approach in creating clusters. Clusters are then considered a set of elements in classes and their centroids are the stems.
Inflectional & Derivational Methods
Krovetz Stemmer: Changes words to word stems that are valid English words.
Xerox Stemmer: Removes prefixes.[11]
Term Frequency
Term Frequency Inverse Document Frequency
Topic Modeling
Latent Semantic Analysis (LSA)
Latent Dirichlet Allocation (LDA)
Non-Negative Matrix Factorization (NMF)
Bidirectional Encoder Representations from Transformers (BERT)
Wordscores: First estimates scores on word types based on a reference text. Then applies wordscores to a text that is not a reference text to get a document score. Lastly, documents that are not referenced are rescaled to then compare to the reference text.[12]
^"Different Types of Clustering Algorithm". GeeksforGeeks. 2018-01-15. Retrieved 2024-04-04.
^Jalil, Abdennour Mohamed; Hafidi, Imad; Alami, Lamiae; Khouribga, Ensa (2016). "Comparative Study of Clustering Algorithms in Text Mining Context". International Journal of Interactive Multimedia and Artificial Intelligence. 3 (7): 42. doi:10.9781/ijimai.2016.376. ISSN 1989-1660.
^Jalil, Abdennour Mohamed; Hafidi, Imad; Alami, Lamiae; Khouribga, Ensa (2016). "Comparative Study of Clustering Algorithms in Text Mining Context". International Journal of Interactive Multimedia and Artificial Intelligence. 3 (7): 42. doi:10.9781/ijimai.2016.376. ISSN 1989-1660.
^Jalil, Abdennour Mohamed; Hafidi, Imad; Alami, Lamiae; Khouribga, Ensa (2016). "Comparative Study of Clustering Algorithms in Text Mining Context". International Journal of Interactive Multimedia and Artificial Intelligence. 3 (7): 42. doi:10.9781/ijimai.2016.376. ISSN 1989-1660.
^Jalil, Abdennour Mohamed; Hafidi, Imad; Alami, Lamiae; Khouribga, Ensa (2016). "Comparative Study of Clustering Algorithms in Text Mining Context". International Journal of Interactive Multimedia and Artificial Intelligence. 3 (7): 42. doi:10.9781/ijimai.2016.376. ISSN 1989-1660.
^Jalil, Abdennour Mohamed; Hafidi, Imad; Alami, Lamiae; Khouribga, Ensa (2016). "Comparative Study of Clustering Algorithms in Text Mining Context". International Journal of Interactive Multimedia and Artificial Intelligence. 3 (7): 42. doi:10.9781/ijimai.2016.376. ISSN 1989-1660.
^"Agglomerative Methods in Machine Learning". GeeksforGeeks. 2021-02-01. Retrieved 2024-04-04.
^"Agglomerative Methods in Machine Learning". GeeksforGeeks. 2021-02-01. Retrieved 2024-04-04.
^Hahsler, Michael; et al. "dbscan: Fast Density-based Clustering with R" (PDF). cran.r-project.org. Retrieved 4 March 2024.
^"Different Types of Clustering Algorithm". GeeksforGeeks. 2018-01-15. Retrieved 2024-04-04.
^Ganesh Jivani, Anjali. "A Comparative Study of Stemming Algorithms" (PDF).
^Lowe, Will (2008). "Understanding Wordscores" (PDF). Methods and Data Institute, School of Politics and International Relations, University of Nottingham, Nottingham. doi:10.2139/ssrn.1095280. ISSN 1556-5068.
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