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

Exam (elaborations) TEST BANK FOR Data Mining Concepts and Techniques 2nd Edition By Jiawei Han, Micheline Kamber [Solution Manual] Data Mining: Concepts and Techniques 2nd Edition Solution Manual Jiawei Han and Micheline Kamber The University of Illinois at Urbana-Champaign °c Morgan Kaufmann, 2006 Note: For Instructors' reference only. Do not copy! Do not distribute! Contents 1 Introduction 3 1.11 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Data Preprocessing 13 2.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3 Data Warehouse and OLAP Technology: An Overview 31 3.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4 Data Cube Computation and Data Generalization 41 4.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5 Mining Frequent Patterns, Associations, and Correlations 53 5.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 6 Classi¯cation and Prediction 69 6.17 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 7 Cluster Analysis 79 7.13 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 8 Mining Stream, Time-Series, and Sequence Data 91 8.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 9 Graph Mining, Social Network Analysis, and Multirelational Data Mining 103 9.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 10 Mining Object, Spatial, Multimedia, Text, and Web Data 111 10.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 11 Applications and Trends in Data Mining 123 11.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 1 Chapter 1 Introduction 1.11 Exercises 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 representing a student. The rule indicates that of the students under study, 12% (support) major in computing science and own a personal computer. There is a 98% probability (con¯dence, or certainty) that a student in this group owns a personal computer. ² Classi¯cation di®ers from prediction in that the former constructs a set of models (or functions) that describe and distinguish data classes or concepts, whereas the latter builds a model to predict some missing or unavailable, and often numerical, data values. Their similarity is that they are both tools for prediction: Classi¯cation is used for predicting the class label of data objects and prediction is typically used for predicting missing numerical data values. ² Clustering analyzes data objects without consulting a known class label. The objects are clustered or grouped based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. Each cluster that is formed can be viewed as a class of objects. Clustering can also facilitate taxonomy formation, that is, the organization of observations into a hierarchy of classes that group similar events together. ² Data evolution analysis describes and models regularities or trends for objects whose behavior changes over time. Although this may include characterization, discrimination, association, classi¯ca- tion, or clustering of time-related data, distinct features of such an analysis include time-series data analysis, sequence or periodicity pattern matching, and similarity-based data analysis. 1.7. What is the di®erence between discrimination and classi¯cation? Between characterization and clustering? Between classi¯cation and prediction? For each of these pairs of tasks, how are they similar? Answer: ² Discrimination di®ers from classi¯cation in that the former refers to 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, while the latter is the process of ¯nding a set of models (or functions) that describe and distinguish data classes or concepts for the purpose of being able to use the model to predict the class of objects whose class label is unknown. Discrimination and classi¯cation are similar in that they both deal with the analysis of class data objects. ² Characterization di®ers from clustering in that the former refers to a summarization of the general characteristics or features of a target class of data while the latter deals with the analysis of data objects without consulting a known class label. This pair of tasks is similar in that they both deal with grouping together objects or data that are related or have high similarity in comparison to one another. ² Classi¯cation di®ers from prediction in that the former is the process of ¯nding a set of models (or functions) that describe and distinguish data class or concepts while the latter predicts missing or unavailable, and often numerical, data values. This pair of tasks is similar in that they both are tools for prediction: Classi¯cation is used for predicting the class label of data objects and prediction is typically used for predicting missing numerical data values. 1.11. EXERCISES 7 1.8. Based on your observation, describe another possible kind of knowledge that needs to be discovered by data mining methods but has not been listed in this chapter. Does it require a mining methodology that is quite di®erent from those outlined in this chapter? Answer: There is no standard answer for this question and one can judge the quality of an answer based on the freshness and quality of the proposal. For example, one may propose partial periodicity as a new kind of knowledge, where a pattern is partial periodic if only some o®sets of a certain time period in a time series demonstrate some repeating behavior. 1.9. List and describe the ¯ve primitives for specifying a data mining task. Answer: The ¯ve primitives for specifying a data-mining task are: ² Task-relevant data: This primitive speci¯es the data upon which mining is to be performed. It involves specifying the database and tables or data warehouse containing the relevant data, conditions for selecting the relevant data, the relevant attributes or dimensions for exploration, and instructions regarding the ordering or grouping of the data retrieved. ² Knowledge type to be mined: This primitive speci¯es the speci¯c data mining function to be performed, such as characterization, discrimination, association, classi¯cation, clustering, or evolution analysis. As well, the user can be more speci¯c and provide pattern templates that all discovered patterns must match. These templates, or metapatterns (also called metarules or metaqueries), can be used to guide the discovery process. ² Background knowledge: This primitive allows users to specify knowledge they have about the domain to be mined. Such knowledge can be used to guide the knowledge discovery process and evaluate the patterns that are found. Concept hierarchies and user beliefs regarding relationships in the data are forms of background knowledge. ² Pattern interestingness measure: This primitive allows users to specify functions that are used to separate uninteresting patterns from knowledge and may be used to guide the mining process, as well as to evaluate the discovered patterns. This allows the user to con¯ne the number of uninteresting patterns returned by the process, as a data mining process may generate a large number of patterns. Interestingness measures can be speci¯ed for such pattern characteristics as simplicity, certainty, utility and novelty. ² Visualization of discovered patterns: This primitive refers to the form in which discovered patterns are to be displayed. In order for data mining to be e®ective in conveying knowledge to users, data mining systems should be able to display the discovered patterns in multiple forms such as rules, tables, cross tabs (cross-tabulations), pie or bar charts, decision trees, cubes or other visual representations. 1.10. Describe why concept hierarchies are useful in data mining. Answer: Concept hierarchies de¯ne a sequence of mappings from a set of lower-level concepts to higher-level, more general concepts and can be represented as a set of nodes organized in a tree, in the form of a lattice, or as a partial order. They are useful in data mining because they allow the discovery of knowledge at multiple levels of abstraction and provide the structure on which data can be generalized (rolled-up) or specialized (drilled-down). Together, these operations allow users to view the data from di®erent perspectives, gaining further insight into relationships hidden in the data. Generalizing has the advantage of compressing the data set, and mining on a compressed data set will require fewer I/O operations. This will be more e±cient than mining on a large, uncompressed data set. 8 CHAPTER 1. INTRODUCTION 1.11. Outliers are often discarded as noise. However, one person's garbage could be another's treasure. For example, exceptions in credit card transactions can help us detect the fraudulent use of credit cards. Taking fraudulence detection as an example, propose two methods that can be used to detect outliers and discuss which one is more reliable. Answer: ² Using clustering techniques: After clustering, the di®erent clusters represent the di®erent kinds of data (transactions). The outliers are those data points that do not fall into any cluster. Among the various kinds of clustering methods, density-based clustering may be the most e®ective. Clustering is detailed in Chapter 8. ² Using prediction (or regression) techniques: Constructed a probability (regression) model based on all of the data. If the predicted value for a data point di®ers greatly from the given value, then the given value may be consider an outlier. Outlier detection based on clustering techniques may be more reliable. Because clustering is unsupervised, we do not need to make any assumptions regarding the data distribution (e.g., density-based methods). In contrast, regression (prediction) methods require us to make some assumptions of the data distribution, which may be inaccurate due to insu±cient data. 1.12. Recent applications pay special attention to spatiotemporal data streams. A spatiotemporal data stream contains spatial information that changes over time, and is in the form of stream data, i.e., the data °ow in-and-out like possibly in¯nite streams. (a) Present three application examples of spatiotemporal data streams. (b) Discuss what kind of interesting knowledge can be mined from such data streams, with limited time and resources. (c) Identify and discuss the major challenges in spatiotemporal data mining. (d) Using one application example, sketch a method to mine one kind of knowledge from such stream data e±ciently. Answer: (a) Present three application examples of spatiotemporal data streams. i. Sequences of sensor images of a geographical region along time. ii. The climate images from satellites. iii. Data that describe the evolution of natural phenomena, such as forest coverage, forest ¯re, and so on. (b) Discuss what kind of interesting knowledge can be mined from such data streams, with limited time and resources. The knowledge that can be mined from spatiotemporal data streams really depends on the application. However, one unique type of knowledge about stream data is the patterns of spatial change with respect to the time. For example, the changing of the tra±c status of several highway junctions in a city, from the early morning to rush hours and back to o®-peak hours, can show clearly where the tra±c comes from and goes to and hence, would help the tra±c o±cer plan e®ective alternative lanes in order to reduce the tra±c load. As another example, a sudden appearance of a point in the spectrum space image may indicate that a new planet is being formed. The changing of humidity, temperature, and pressure in climate data may reveal patterns of how a new typhoon is created. (c) Identify and discuss the major challenges in spatiotemporal data mining. One major challenge is how to deal with the continuing large-scale data. Since the data keep °owing in and each snapshot of data is usually huge (e.g., the spectrum image of space), it is [old: almost][new: 1.11. EXERCISES 9 often] impossible to store all of the data. Some aggregation or compression techniques may have to be applied, and old raw data may have to be dropped. Mining under such aggregated (or lossy) data is challenging. In addition, some patterns may occur with respect to a long time period, but it may not be possible to keep the data for such a long duration. Thus, these patterns may not be uncovered. The spatial data sensed may not be so accurate, so the algorithms must have high tolerance with respect to noise. (d) Using one application example, sketch a method to mine one kind of knowledge from such stream data e±ciently. Take mining space images as the application. We seek to observe whether any new planet is being created or any old planet is disappearing. This is a change detection problem. Since the image frames keep coming, that is, f1; : : : ; ft; ft+1; : : :, we can simplify the overall problem to that of detecting whether any planet appears or disappears between two consecutive image frames, ft and ft+1. The algorithm can be sketched as follows. For each incoming frame, ft+1, compare it with the previous frame, ft. i. Match the planets in ft+1 with ft. ii. Detect whether there are any unmatched" planets (where a planet in one of the two frames does not occur in the other). iii. If yes, report a planet appearance (if an unmatched planet appears in the new frame) or a planet disappearance (if an unmatched planet appears in the old frame). In fact, matching between two frames may not be easy because the earth is rotating and thus, the sensed data may have slight variations. Some advanced techniques from image processing may be applied. The overall skeleton of the algorithm is simple. Each new incoming image frame is only compared with the previous one, satisfying the time and resource constraint. The reported change would be useful since it is [old: almost impossible][new: infeasible] for astronomers to dig into every frame to detect whether a new planet has appeared or an old one has disappeared. 1.13. Describe the di®erences between the following approaches for the integration of a data mining system with a database or data warehouse system: no coupling, loose coupling, semitight coupling, and tight coupling. State which approach you think is the most popular, and why. Answer: The di®erences between the following architectures for the integration of a data mining system with a database or data warehouse system are as follows. ² No