The idea of text clustering long preceded the computer age: “Clustering is one of the most primitive mental activities of humans, used to handle the huge amount of information they receive every day” (Theodoridis and Koutroubas, 2003: 398). The act of indexing long used in libraries is an obvious example. Manual clustering was the only type of document clustering possible prior to the computer age. This circumstance may have influenced much clustering work that relied only on immediate intuitive knowledge of the world without making use of quantitative numerical methods. In other words, text clustering was usually performed in subjective ways that relied heavily on the perception, knowledge, and judgment of the researcher. With more and easier accessibility to electronic digital data in different disciplines and the power of computing data processing on one hand and the need for maintaining objectivity standards on the other, it has become ever more likely that such procedures must involve computational automated methods (Arabie et al., 1996) where human intuition and traditional organization methods are replaced by mathematical and computational techniques (Golub, 2006; Golub, 2005). In this, recent years have witnessed a flourishing of the development of automated statistical clustering and classification systems for systematizing the inherent subjectivity in traditional text classification applications. It is this need for automated objective methodology that motivates our clustering of Hardy’s novels and short stores. Clustering vs. classification The two terms clustering and classification are extensively used throughout this thesis. The question that rises at this point is: are they synonymous or is there a distinction... ... middle of paper ... ...ion is that clustering is an “unsupervised” activity while classification is a supervised one. In clustering, there is no one who assigns documents to classes but it is only the distribution and makeup of the data that will determine cluster membership (Manning et al., 2008). To illustrate the argument, let us consider the following example. Having a set of 1000 documents on the history of English literature, these can be both clustered and classified. In performing a clustering task, documents are just clustered into distinct groups where similar or related documents are grouped together. In classification, on the other hand, predefined sets are given first. These can be Old English literature, Shakespearean literature, Augustan Literature, Romantic Literature, and Victorian Literature. Then documents are placed or classified under these predefined categories.
1. What is the name of the document? Ida Tarbell Criticizes Standard Oil (1904) 2. What type of document is it? (newspaper, map, image, report, Congressional record, etc.)
Jean Carletta, “Assessing agreement on classification tasks: The kappa statistic”. Computational Linguistics, MIT Press Cambridge, MA, USA, Vol. 22, No.2, pp. 249–254, 1996.
The diversity among people is widely spread throughout the world. One can be grouped into various ways. People come in all shapes, sizes, colors, personalities, genders, and interests. LIfe would be hectic for someone to try and categorize people in every way possible. At least people are not the only thing impossible to fully separate. Animals and plants can also be placed into different categories. People can be classified into three categories: Leaders, Followers, and Independents.
Paternoster and Bach,. (2013, May. 28 ). In Oxford Bibliographies Online. (chap. Labeling Theory - CrLabeling Theory) Retrieved Oct. 27, 2013, from http://www.oxfordbibliographies.com/view/document/
Classification Text documents are arranged into groups of pre-labeled class. Learning schemes learn through training text documents and efficiency of these system is tested by using test text documents. Common algorithms include decision tree learning, naive Bayesian classification, nearest neighbor and neural network. This is called supervised learning.
While the Dewey decimal system contains a comprehensive index, the Library of Congress Classification system does not (Taylor 430). Each volume of the LCC schedules contains its own index and these indexes do not refer to one another. Finding subjects in the schedules can be awkward. To locate a topic, one must check through each volume index of all the different disciplines that may ...
o The terms of the classification tell us what the individuals in that class have in common.
4. Cladistics and evolutionary systematics are two approaches to classification. How are they similar and how are they different? What are the benefits of using one over another?
Bloom, B.S, Rehage Kenneth J., Anderson, Lorin W. (1994) Bloom’s taxonomy: A forty- year retrospective. Chicago:NSSE.
> All of these categories are about how we identify with others based on similarities.
literature can be better understood in the light of such concepts or clusters of concepts as
One of these is Social categorization. Based on the name it is very self explanatory, with the social categorization there is the need to divide, categorizing individuals into groups (in and out groups).
As well, another principle would be similarity and how it is identified as the tendency to perceive things that look similar as being part of the same group. For example, as early as grade school people are trained to be able to see similar things that go together, such as objects being the
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
Then classification is performed on the basis of similarity score of a class with respect to a neighbor.