Exam (elaborations) TEST BANK FOR Data Mining Concepts and Techniques 2nd Edition By Jiawei Han, Micheline Kamber [Solution Manual].-Converted

1.1. What is data mining? In your answer, address the following: (a) Is it another hype? (b) Is it a simple transformation of technology developed from databases, statistics, and machine learning? (c) Explain how the evolution of database technology led to data mining. (d) Describe the steps involved in data mining when viewed as a process of knowledge discovery. Answer: Data mining refers to the process or method that extracts or mines" interesting knowledge or patterns from large amounts of data. (a) Is it another hype? Data mining is not another hype. Instead, the need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. Thus, data mining can be viewed as the result of the natural evolution of information technology. (b) Is it a simple transformation of technology developed from databases, statistics, and machine learning? No. Data mining is more than a simple transformation of technology developed from databases, sta- tistics, and machine learning. Instead, data mining involves an integration, rather than a simple transformation, of techniques from multiple disciplines such as database technology, statistics, ma- chine learning, high-performance computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial data analysis. (c) Explain how the evolution of database technology led to data mining. Database technology began with the development of data collection and database creation mechanisms that led to the development of e®ective mechanisms for data management including data storage and retrieval, and query and transaction processing. The large number of database systems o®ering query and transaction processing eventually and naturally led to the need for data analysis and understanding. Hence, data mining began its development out of this necessity. (d) Describe the steps involved in data mining when viewed as a process of knowledge discovery. The steps involved in data mining when viewed as a process of knowledge discovery are as follows: ² Data cleaning, a process that removes or transforms noise and inconsistent data ² Data integration, where multiple data sources may be combined 3 4 CHAPTER 1. INTRODUCTION ² Data selection, where data relevant to the analysis task are retrieved from the database ² Data transformation, where data are transformed or consolidated into forms appropriate for mining ² Data mining, an essential process where intelligent and e±cient methods are applied in order to extract patterns ² Pattern evaluation, a process that identi¯es the truly interesting patterns representing knowl- edge based on some interestingness measures ² Knowledge presentation, where visualization and knowledge representation techniques are used to present the mined knowledge to the user 1.2. Present an example where data mining is crucial to the success of a business. What data mining functions does this business need? Can they be performed alternatively by data query processing or simple statistical analysis? Answer: A department store, for example, can use data mining to assist with its target marketing mail campaign. Using data mining functions such as association, the store can use the mined strong association rules to determine which products bought by one group of customers are likely to lead to the buying of certain other products. With this information, the store can then mail marketing materials only to those kinds of customers who exhibit a high likelihood of purchasing additional products. Data query processing is used for data or information retrieval and does not have the means for ¯nding association rules. Similarly, simple statistical analysis cannot handle large amounts of data such as those of customer records in a department store. 1.3. Suppose your task as a software engineer at Big-University is to design a data mining system to examine their university course database, which contains the following information: the name, address, and status (e.g., undergraduate or graduate) of each student, the courses taken, and their cumulative grade point average (GPA). Describe the architecture you would choose. What is the purpose of each component of this architecture? Answer: A data mining architecture that can be used for this application would consist of the following major components: ² A database, data warehouse, or other information repository, which consists of the set of databases, data warehouses, spreadsheets, or other kinds of information repositories containing the student and course information. ² A database or data warehouse server, which fetches the relevant data based on the users' data mining requests. ² A knowledge base that contains the domain knowledge used to guide the search or to evaluate the interestingness of resulting patterns. For example, the knowledge base may contain concept hierarchies and metadata (e.g., describing data from multiple heterogeneous sources). ² A data mining engine, which consists of a set of functional modules for tasks such as classi¯cation, association, classi¯cation, cluster analysis, and evolution and deviation analysis. ² A pattern evaluation module that works in tandem with the data mining modules by employing interestingness measures to help focus the search towards interesting patterns. ² A graphical user interface that provides the user with an interactive approach to the data mining system. 1.11. EXERCISES 5 1.4. How is a data warehouse di®erent from a database? How are they similar? Answer: ² Di®erences between a data warehouse and a database: A data warehouse is a repository of informa- tion collected from multiple sources, over a history of time, stored under a uni¯ed schema, and used for data analysis and decision support; whereas a database, is a collection of interrelated data that rep- resents the current status of the stored data. There could be multiple heterogeneous databases where the schema of one database may not agree with the schema of another. A database system supports ad-hoc query and on-line transaction processing. Additional di®erences are detailed in Section 3.1.1 Di®erences between Operational Databases Systems and Data Warehouses. ² Similarities between a data warehouse and a database: Both are repositories of information, storing huge amounts of persistent data. 1.5. Brie°y describe the following advanced database systems and applications: object-relational databases, spatial databases, text databases, multimedia databases, the World Wide Web. Answer: ² An objected-oriented database is designed based on the object-oriented programming paradigm where data are a large number of objects organized into classes and class hierarchies. Each entity in the database is considered as an object. The object contains a set of variables that describe the object, a set of messages that the object can use to communicate with other objects or with the rest of the database system, and a set of methods where each method holds the code to implement a message. ² A spatial database contains spatial-related data, which may be represented in the form of raster or vector data. Raster data consists of n-dimensional bit maps or pixel maps, and vector data are represented by lines, points, polygons or other kinds of processed primitives, Some examples of spatial databases include geographical (map) databases, VLSI chip designs, and medical and satellite images databases. ² A text database is a database that contains text documents or other word descriptions in the form of long sentences or paragraphs, such as product speci¯cations, error or bug reports, warning messages, summary reports, notes, or other documents. ² A multimedia database stores images, audio, and video data, and is used in applications such as picture content-based retrieval, voice-mail systems, video-on-demand systems, the World Wide Web, and speech-based user interfaces. ² The World Wide Web provides rich, world-wide, on-line information services, where data objects are linked together to facilitate interactive access. Some examples of distributed information services associated with the World Wide Web include America Online, Yahoo!, AltaVista, and Prodigy. 1.6. De¯ne each of the following data mining functionalities: characterization, discrimination, association and correlation analysis, classi¯cation, prediction, clustering, and evolution analysis. Give examples of each data mining functionality, using a real-life database that you are familiar with. Answer: ² Characterization is a summarization of the general characteristics or features of a target class of data. For example, the characteristics of students can be produced, generating a pro¯le of all the University ¯rst year computing science students, which may include such information as a high GPA and large number of courses taken. ² Discrimination is a comparison of the general features of target class data objects with the general features of objects from one or a set of contrasting classes. For example, the general features of students with high GPA's may be compared with the general features of students with low GPA's. The resulting 6 CHAPTER 1. INTRODUCTION description could be a general comparative pro¯le of the students such as 75% of the students with high GPA's are fourth-year computing science students while 65% of the students with low GPA's are not. ² Association is the discovery of association rules showing attribute-value conditions that occur fre- quently together in a given set of data. For example, a data mining system may ¯nd association rules like major(X; computing science"") ) owns(X; personal computer") [support = 12%; confidence = 98%] where X is a variable