An Overview on Data Mining

In this paper, we present an overview of research issues in Data mining. We discuss mining with respect to web data referred here as web data mining. In particular, our focus is on web data mining research in context of our web Data Mining project called ‘A Study on Web Data Mining’. We have categorized web data mining into three areas; web content mining, web structure mining and web usage mining. We have highlighted and discussed various research issues involved in each of these web data mining category. We believe that web data mining will be the topic of exploratory research in near future. INTRODUCTION The advent of the World Wide Web has caused a dramatic increase in the usage of the Internet. The World Wide Web is a broadcast medium where a wide range of information can be obtained at a low cost. Information on the WWW is important not only to individual users, but also to the business organizations especially when the critical decision-making is concerned. Most users obtain WWW information using a combination of search engines and browser; however, these two types of retrieval mechanisms do not necessarily address all of a user’s information needs. This is particularly true in the case of business organizations that currently lack suitable tools to systematically harness strategic information from the web and analyze these data to discover useful knowledge to support decision making. A recent study provides a comprehensive and comparative evaluation of the most popular search engines. A more recent survey of web query processing has appeared. The resulting growth in on-line information combined with the almost unstructured web data necessitates the development of powerful yet computationally efficient web data mining tools. Web data mining can be defined as the discovery and analysis of useful information from the WWW data. Web involves three types of data; data on the WWW, the web log data regarding the users who browsed the web pages and the web structure data. WHY DATA MINING? The Web—an immense and dynamic collection of pages that includes countless hyperlinks and huge volumes of access and usage information—provides a rich and unprecedented data mining source. However, the Web also poses several challenges to effective resource and knowledge discovery: • Web page complexity far exceeds the complexity of any traditional text document collection. Although the Web functions as a huge digital library, the pages themselves lack a uniform structure and contain far more authoring style and content variations than any set of books or traditional text-based documents. Moreover, the tremendous number of documents in this digital library has not been indexed, which makes searching the data it contains extremely difficult. • The Web constitutes a highly dynamic information source. Not only does the Web continue to grow rapidly, the information it holds also receives constant updates. News, stock market, service center, and corporate sites revise their Web pages regularly. Linkage information and access records also undergo frequent updates. • The Web serves a broad spectrum of user communities. The Internet’s rapidly expanding user community connects millions of workstations. These users have markedly different backgrounds, interests, and usage purposes. Many lack good knowledge of the information network’s structure, are unaware of a particular search’s heavy cost, frequently get lost within the Web’s ocean of information, and can chafe at the many access hops and lengthy waits required to retrieve search results. • Only a small portion of the Web’s pages contain truly relevant or useful information. A given user generally focuses on only a tiny portion of the Web, dismissing the rest as uninteresting data that serves only to swamp the desired search results. How can a search identify that portion of the Web that is truly relevant to one user’s interests? How can a search find high-quality Web pages on a specified topic? DATA MINING Data mining is the process of discovering actionable information from large sets of data. Data mining uses mathematical analysis to derive patterns and trends that exist in data. Typically, these patterns cannot be discovered by traditional data exploration because the relationships are too complex or because there is too much data. Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD). The key properties of data mining are:  Automatic discovery of patterns  Prediction of likely outcomes  Creation of actionable information  Focus on large data sets and databases Vishal et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 1269-1272