Data Mining and Knowledge Engineering

Deepa John ¹, G. Naveen Sundar ¹

Affiliations
1 Karunya University, Coimbatore, IN

Abstract

Internet provides huge amount of information. The amount of information on the web is growing at an astonishing rate. Web can be considered as the largest knowledge base. Web pages contain a lot of information. Extracting data from the web pages are very difficult. This is mainly because of the complex structure of the web pages. And there isn’t any uniformity when the structure of the web page is considered. Due to the lack of any uniform structure of Web information sources, access to this huge collection of information has been limited to browsing and searching. Many a times the data need to be extracted from the web pages so as to facilitate different applications. Also, extracting relevant data alone is a tedious task. Therefore, the availability of robust, flexible extraction methods that transform the Web pages into program-friendly structures such as a relational database has become a great necessity. Although many approaches for data extraction from Web pages have been developed, there has been limited effort to compare such tools. This survey paper mentions some of the techniques for web data extraction.

Keywords

Semi-Structured Data, Data Extraction, Web Database, Web Mining, Wrapper Generation.

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Navitha Varghese ¹, G. Naveen Sundar ¹

Affiliations
1 Department of Computer Science and Engineering, Karunya University, Coimbatore, Tamil Nadu, IN

Abstract

Human behavior can be characterized by a set of sequential action patterns. As such, there can be a causal relationship among actions. When these actions take place in uncertain conditions, it is difficult to predict the next action based on the observed actions. Thus, it would be a challenging task to establish some causal relationships among the sequential actions under observation .Some techniques are used for labeling actions which also deals with predicting actions. Further, we want to point out potential pitfalls as well as challenging issues of different techniques. We believe that the results of this evaluation will help for building up an autonomous system.

Keywords

Prediction, HMM, CRF, AIBFC, Human Behavior.

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