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Advantages And Disadvantages Of Text Mining

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4. TEXT CLASSIFICATION IN ASIAN LANGUAGES WITHOUT WORD SEGMENTATION

Experimental paradigm

Both of the Chinese and Japanese data sets involve classifying into a large number of categories, where each document is assigned a single category. Many classification techniques, such as SVMs, are intrinsically defined for two class problems, and have to be extended to handle these multiple category data sets. For SVMs, we employ a standard technique of first converting the |C| category classification problem to |C| binary classification problems. For the experiments on Chinese data, we follow (He et al., 2001) and convert the problem into 6 binary classification problems. In each case, we randomly select 500 positive examples and then select 500 negative examples evenly from among the remaining negative categories to form the …show more content…

OTHER RESEARCHES

Various researchers have also implemented various novel techniques in order to enrich the performance of the text mining operations. Table 1 discusses about the existing techniques, performance parameters used and the limitations being observed in the implementation papers for text-mining.

1. Gupta and Lehal [13]:
The authors have presented a review of various techniques on text-mining. Following are the respective inferences:
a. Pros: Discusses good theoretical details about the operations and the applications of Text Data Mining
b. Cons: There was no discussion on performance of techniques.

2. Clifton et al. [14]:
The authors have performed review of various security techniques on datamining.
a. Pros: Performance measurements as well as various Methods for the domain study have been done in depth.
b. Cons: Extremely narrowed discussion about the prior techniques.

3. Clifton and Zhai. [15]:
The authors have investigated about the various clustering mechanism involved in text mining approach
a. Pros: Detailed discussion about the domain study.
b. Cons: Very less discussion about the performance

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