Data Mining Mehmed Kantardzic (good english books to read .txt) 📖

concepts described in the document. Mining a concept-based IF derives patterns and relationships across objects and concepts. These semantic-analysis methods are computationally expensive, and it is a challenge to make them more efficient and scalable for very large text corpora. Text-mining operations such as predictive modeling and association discovery fall in this category. A document-based IF can be transformed into a concept-based IF by realigning or extracting the relevant information according to the objects of interests in a specific domain. It follows that a document-based IF is usually domain-independent and a concept-based is a domain-dependent representation.

Text-refining and knowledge-distillation functions as well as the IF adopted are the basis for classifying different text-mining tools and their corresponding techniques. One group of techniques, and some recently available commercial products, focuses on document organization, visualization, and navigation. Another group focuses on text-analysis functions, IR, categorization, and summarization.

An important and large subclass of these text-mining tools and techniques is based on document visualization. The general approach here is to organize documents based on their similarities and present the groups or clusters of the documents as 2-D or 3-D graphics. IBM’s Intelligent Miner and SAS Enterprise Miner are probably the most comprehensive text-mining products today. They offer a set of text-analysis tools that include tools for feature-extraction, clustering, summarization, and categorization; they also incorporate a text search engine. More examples of text-mining tools are given in Appendix A.

Domain knowledge, not used and analyzed by any currently available text-mining tool, could play an important role in the text-mining process. Specifically, domain knowledge can be used as early as in the text-refining stage to improve parsing efficiency and derive a more compact IF. Domain knowledge could also play a part in knowledge distillation to improve learning efficiency. All these ideas are still in their infancy, and we expect that the next generation of text-mining techniques and tools will improve the quality of information and knowledge discovery from text.

11.7 LATENT SEMANTIC ANALYSIS (LSA)

LSA is a method that was originally developed to improve the accuracy and effectiveness of IR techniques by focusing on semantic meaning of words across a series of usage contexts, as opposed to using simple string-matching operations. LSA is a way of partitioning free text using a statistical model of word usage that is similar to eigenvector decomposition and factor analysis. Rather than focusing on superficial features such as word frequency, this approach provides a quantitative measure of semantic similarities among documents based on a word’s context.

Two major shortcomings to the use of term counts are synonyms and polysemes. Synonyms refer to different words that have the same or similar meanings but are entirely different words. In the case of the vector approach, no match would be found between a query using the term “altruistic” and a document using the word “benevolent” though the meanings are quite similar. On the other hand, polysemes are words that have multiple meanings. The term “bank” could mean a financial system, to rely upon, or a type of basketball shot. All of these lead to very different types of documents, which can be problematic for document comparisons.

LSA attempts to solve these problems, not with extensive dictionaries and natural language processing engines, but by using mathematical patterns within the data itself to uncover these relationships. We do this by reducing the number of dimensions used to represent a document using a mathematical matrix operation called singular value decomposition (SVD).

Let us take a look at an example data set. This very simple data set consists of five documents. We will show the dimension reduction steps of LSA on the first four documents (), which will make up our training data. Then we will make distance comparisons to a fifth document () in our test set, using the nearest neighbor classification approach. The initial document set is:

d1: A bank will protect your money.

d2: A guard will protect a bank.

d3: Your bank shot is money.

d4: A bank shot is lucky.

d5: bank guard.

From the text data we derive a vector representation of our documents using only term counts. This vector representation could be thought of as a matrix with rows representing terms and columns representing documents. This representation may be seen in Figure 11.7.

Figure 11.7. Initial term counts.

The first step of LSA is to decompose the matrix representing our original data set of four documents, matrix A, using SVD as follows: A = USVT. The calculation of SVD is beyond the scope of this text, but there are several computing packages that will perform this operation for you (such as R or MATLAB packages). The matrices resulting from this decomposition can be seen in Figure 11.8. The U and VT matrices provide a vector of weights for terms and documents, respectively. Consider VT, this matrix gives a new 4-D representation to each document where each document is described with a corresponding column. Each of the new dimensions is derived from the original 10 word count dimensions. For example, document 1 is now represented as follows: d1 = −0.56x1 + 0.095x2 − 0.602x3 + 0.562x4, where each xn represents one of the new, derived dimensions. The S matrix is a diagonal matrix of eigenvalues for each principal component direction.

Figure 11.8. Singular value decomposition of initial data.

With this initial step, we have already reduced the number of dimensions representing each document from 10 to four. We now show how one would go about further reducing the number of dimensions. When the data set contains many documents, the previous step is not enough to reduce the number of dimensions meaningfully. To perform further reduction, we first observe that matrix S provides us with a diagonal matrix of eigenvalues in descending order as follows: λ1, … , λ4 = {3.869, 2.344, 1.758, 0.667}. We will be able to capture most of the variability of the data by retaining only the first k eigenvalues rather than all n terms (in our matrix S, n = 4). If for example k =