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 (a number of clusters) 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 in to smaller clusters.
  • Density-based Clustering: A structure is determined by the density of data points.
    • DBSCAN

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    Distribution-based Clustering: Clusters are formed based off 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 off 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.