science

List of text mining methods

List of text mining methods is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand List of text mining methods rather than just read about it. In short: Text mining methods are different forms of text mining whose usage is based on their suitability for a given data set. Text mining is the process of extracting data from unstructured text and finding patterns or relations.

Key takeaways

List of text mining methods belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect List of text mining methods to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of List of text mining methods from memory before moving on to harder problems.

Reference excerpt

Text mining methods are different forms of text mining whose usage is based on their suitability for a given 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 K-Means: Made to accelerate Global K-Means. Global K-Means: Global K-Means is an algorithm that begins with one cluster, and then divides into multiple clusters based on the number required. K-Means: An algorithm that requires two parameters: K, a number of clusters, and a set of data. FW-K-Means: Used with vector space model. Uses the methodology of weight to decrease noise. Two-Level-K-Means: Regular K-Means 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 starts 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.

References

Illustrations

Worked examples

Example 1 — a first encounter with List of text mining methods

Start with the simplest possible case. Write down what List of text mining methods claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to List of text mining methods before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about List of text mining methods ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of List of text mining methods

In research: List of text mining methods appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses List of text mining methods in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: List of text mining methods is common in secondary-school and first-year university syllabi. It links to neighbouring topics Text mining, so understanding it makes those chapters shorter.
In everyday life: Look for List of text mining methods outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study List of text mining methods in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what List of text mining methods means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain List of text mining methods out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is List of text mining methods in simple terms?

Why does List of text mining methods matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study List of text mining methods?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on List of text mining methods.