6 Text Mining Using Oracle Data Mining

6.1.1 Document Classification

Document classification, also known as document categorization, is the process of assigning documents to categories (for example, themes or subjects). A particular document may fit into two or more different categories. This type of classification can often be represented as a multi target classification problem where a supervised model is built for each category.

6.2.1 Classification and Text Mining

A large number of document classification applications fall into one of the following:

• Assigning multiple labels to a document. ODM does not support this case.

• Assigning a document to one of many labels. For example, automatically assigning a mail message to a folder and spam filtering. This application requires multi-class classification.

The Support Vector Machine (SVM) algorithm provides powerful classifiers that have been used successfully in document classification applications. SVM can deal with thousands of features and is easy to train with small or large amounts of data. SVM is known to work well with text data. For more information about SVM, see Chapter 3.

6.2.2 Clustering and Text Mining

Clustering is used frequently in text mining; the main applications of clustering in text mining are

• Taxonomy generation

• Topic extraction

• Grouping the hits returned by a search engine

Clustering can also be used to group textual information with other indications from business databases to provide novel insights.

The current release of ODM supports clustering text features using both the PL/SQL and Java interfaces.

The k-Means clustering algorithm, described in "Enhanced k-Means Algorithm", supports mining text columns.

6.2.3 Feature Extraction and Text Mining

There are two kinds of text mining problems for which feature extraction is useful:

• Extract features from actual text. Oracle Text is designed to solve this kind of problem. ODM also supports feature extraction from text. Most text mining is focused on this problem.

• Extract semantic features or higher-level features from the basic features uncovered when features are extracted from actual text. Statistical techniques, such as single value decomposition (SVD) and non-negative matrix factorization (NMF), are important in solving this kind of problem. Higher-order features can greatly improve the quality of information retrieval, classification, and clustering tasks.

NMF has been found to provide superior text retrieval when compared to SVD and other traditional decomposition methods. NMF takes as input a term-document matrix and generates a set of topics that represent weighted sets of co-occurring terms. The discovered topics form a basis that provides an efficient representation of the original documents. For more information about NMF, see "Algorithm for Feature Extraction".

6.2.4 Association and Text Mining

Association models can be used to uncover the semantic meaning of words. For example, suppose that the word sheep co-occurs with words like sleep, fence, chew, grass, meadow, farmer, and shear. An association model would include rules connecting sheep with these concepts. Inspection of the rules would provide context for sheep in the document collection. Such associations can improve information retrieval engines. For more information about association models, see "Association".

6.2.5 Regression and Text Mining

Regression is most often used in problems that combine text with other types of data. For more information about regression, see "Regression".

6.2.6 Anomaly Detection and Text Mining

One-Class Support Vector Machine can be used to detect or identify novel or anomalous patterns. For more information, see "Anomaly Detection" .