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

the application of the stream-mining techniques. We recognize that a critical review of existing techniques is needed in order to design and develop efficient mining algorithms and data structures that are able to match the processing rate of the mining with the high arrival rate of data streams. Within a unifying set of notations and terminologies, we describe in this paper the efforts and main techniques for mining data streams and present a comprehensive survey of a number of the state-of-the-art algorithms on mining frequent itemsets over data streams.

Goethals B., Frequent Set Mining, in Data Mining and Knowledge Discovery Handbook, Maimon L., Rokach L., ed., Springer, New York, 2005, pp. 377–397.

Frequent sets lie at the basis of many data-mining algorithms. As a result, hundreds of algorithms have been proposed in order to solve the frequent set mining problem. In this chapter, we attempt to survey the most successful algorithms and techniques that try to solve this problem efficiently. During the first 10 years after the proposal of the frequent set mining problem, several hundreds of scientific papers were written on the topic and it seems that this trend is keeping its pace.

Han, J., M. Kamber, Data Mining: Concepts and Techniques, 2nd edition, Morgan Kaufmann, San Francisco, 2006.

This book gives a sound understanding of data-mining principles. The primary orientation of the book is for database practitioners and professionals with emphasis on OLAP and data warehousing. In-depth analysis of association rules and clustering algorithms is the additional strength of the book. All algorithms are presented in easily understood pseudo-code and they are suitable for use in real-world, large-scale data-mining projects including advanced applications such as Web mining and text mining.

11

WEB MINING AND TEXT MINING

Chapter Objectives

Explain the specifics of Web mining.

Introduce a classification of basic Web-mining subtasks.

Illustrate the possibilities of Web mining using Hyperlink-Induced Topic Search (HITS), LOGSOM, and Path Traversal algorithms.

Describe query-independent ranking of Web pages.

Formalize a text-mining framework specifying the refining and distillation phases.

Outline latent semantic indexing.

11.1 WEB MINING

In a distributed information environment, documents or objects are usually linked together to facilitate interactive access. Examples for such information-providing environments include the World Wide Web (WWW) and online services such as America Online, where users, when seeking information of interest, travel from one object to another via facilities such as hyperlinks and URL addresses. The Web is an ever-growing body of hypertext and multimedia documents. As of 2008, Google had discovered 1 trillion Web pages. The Internet Archive, which makes regular copies of many publicly available Web pages and media files, was three petabytes in size as of March 2009. Several billions of pages are added each day to that number. As the information offered in the Web grows daily, obtaining that information becomes more and more tedious. The main difficulty lies in the semi-structured or unstructured Web content that is not easy to regulate and where enforcing a structure or standards is difficult. A set of Web pages lacks a unifying structure and shows far more authoring styles and content variation than that seen in traditional print document collections. This level of complexity makes an “off-the-shelf” database-management and information-retrieval solution very complex and almost impossible to use. New methods and tools are necessary. Web mining may be defined as the use of data-mining techniques to automatically discover and extract information from Web documents and services. It refers to the overall process of discovery, not just to the application of standard data-mining tools. Some authors suggest decomposing Web-mining task into four subtasks:

1. Resource Finding. This is the process of retrieving data, which is either online or offline, from the multimedia sources on the Web, such as news articles, forums, blogs, and the text content of HTML documents obtained by removing the HTML tags.

2. Information Selection and Preprocessing. This is the process by which different kinds of original data retrieved in the previous subtask is transformed. These transformations could be either a kind of preprocessing such as removing stop words and stemming or a preprocessing aimed at obtaining the desired representation, such as finding phrases in the training corpus and representing the text in the first-order logic form.

3. Generalization. Generalization is the process of automatically discovering general patterns within individual Web sites as well as across multiple sites. Different general-purpose machine-learning techniques, data-mining techniques, and specific Web-oriented methods are used.

4. Analysis. This is a task in which validation and/or interpretation of the mined patterns is performed.

There are three factors affecting the way a user perceives and evaluates Web sites through the data-mining process: (1) Web-page content, (2) Web-page design, and (3) overall site design including structure. The first factor is concerned with the goods, services, or data offered by the site. The other factors are concerned with the way in which the site makes content accessible and understandable to its users. We distinguish between the design of individual pages and the overall site design, because a site is not a simply a collection of pages; it is a network of related pages. The users will not engage in exploring it unless they find its structure simple and intuitive. Clearly, understanding user-access patterns in such an environment will not only help improve the system design (e.g., providing efficient access between highly correlated objects, better authoring design for WWW pages), it will also lead to better marketing decisions. Commercial results will be improved by putting advertisements in proper places, better customer/user classification, and understanding user requirements better through behavioral analysis.

No longer are companies interested in Web sites that simply direct traffic and process orders. Now they want to maximize their profits. They want to understand customer preferences and customize sales pitches to individual users. By evaluating a user’s purchasing and browsing patterns, e-vendors want to serve up (in real time) customized menus of attractive offers e-buyers cannot resist. Gathering and aggregating customer information into e-business intelligence is an important task for any company with Web-based activities. e-Businesses expect big profits from improved decision making, and therefore e-vendors line up for