List of text mining methods

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. Fast Global KMeans: Made to accelerate Global KMeans. 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. KMeans: An algorithm that requires two parameters 1. K 2. Set of data. FW-KMeans: Used with vector space model. Uses the methodology of weight to decrease noise. Two-Level-KMeans: Regular KMeans algorithm takes place first. Clusters are then selected for subdivision into subclasses if they do not reach the threshold. Cluster Algorithm Hierarchical Clustering Agglomerative Clustering: Bottom-up approach. Each cluster is small and then aggregates together to form larger clusters. Divisive Clustering: Top-down approach. Large clusters are split into smaller clusters. Density-based Clustering: A structure is determined by the density of data points. DBSCAN Distribution-based Clustering: Clusters are formed based on mathematical methods from data. 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. 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.

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