St.MARTIN S ENGINEERING COLLEGE Dhulapally, Secunderabad-500 014 Subject: DATA WAREHOUSING AND DATA MINING Class : IT III TUTORIAL QUESTION BANK PART A (Short Answer Questions) 1 Define data mining? 2 Explain the definition of data warehouse? 3 Distinguish between data mining and data warehouse? 4 Identify any three functionality of data mining? 5 Interpret major issues in data mining? 6 Name the steps in the process of knowledge discovery? 7 Discuss relational databases? 8 State object oriented Databases? 9 Explain the spatial databases? 10 Contrast heterogeneous databases and legacy databases? 11 Differentiate classification and Prediction? 12 Describe transactional data bases? 13 List the types of data that can be mined? 14 Define data cube? 15 Define multidimensional data mining? 16 Define data characterization? 17 Express what is a decision tree? 18 Explain the outlier analysis? 19 Name the steps involved in data preprocessing? 20 Interpret the dimensionality reduction? 21 A database has five transactions. Let min sup = 60% and min con f =80%. a) Find all frequent itemsets using Apriori and FPgrowth, respectively. Compare the efficiency of the two mining processes. b) List all of the strong association rules (with support s and confidence c) matching the following metarule, where X is a variable representing customers, and itemi denotes variables representing items (e.g., A, B, etc.): x transaction, buys(x, item 1 )^buys(x, item 2 ) buys(x, item 3 ) [s, c]
22 Suppose a user selects a set of attributes, street, country,province or state and city for a dimension location from the database All Electronics. The hierarchical ordering among the attributes: Country(15), province_state(365), city(3567), Street(674,339). 1 Define online analytical processing? 2 List the key features of data warehouse? 3 Define data mart? 4 Define enterprise warehouse? 5 Define virtual warehouse? 6 List the metadata repository? 7 List the various multidimensional models? 8 Explain about the star schema? 9 Explain the snowflake schema? 10 Define about the fact constellation model? 11 Name the OLAP operations? 12 Express what is slice and dice operation? 13 Define Pivot operation? 14 Distinguish between the OLAP Systems and Statistical databases? 15 State the various views of data warehouse design? 16 Define Relational OLAP(ROLAP) servers? 17 Explain Multidimensional OLAP(MOLAP) servers? 18 State what is Hybrid OLAP(HOLAP) servers? 19 Define Attribute-Oriented Induction for data characterization? 20 Define the use of concept hierarchy? 21 Sampling can be used for data reduction.what is the resultant of the stratified sample(according to age). T38 T256 T307 T391 T96 T117 T138 T263 T290 T308 T326 T387 T69 T284 1 Define frequent patterns? 2 Define closed itemset? young Young Young Young middle-age middle-age middle-age middle-age middle-age middle-age middle-age middle-age Senior Senior
3 State maximal frequent itemset? 4 List the techniques of efficiency of Apriori algorithm? 5 Explain ECLAT algorithms usage? 6 Name the pruning strategies in mining closed frequent itemsets? 7 Define substructure of a structural pattern? 8 Interpret the rule of support for itemsets A and B? 9 Classify the confidence rule for itemsets A and B? 10 Define itemset? 11 Name the steps in association rule mining? 12 Explain the join step? 13 Describe the prune step? 14 State how can we mine closed frequent itemsets? 15 Name the pruning strategies of closed frequent itemsets? 16 Explain the two kinds of closure checking? 17 Summarize the constraint-based mining? 18 Describe the five categories of pattern mining constraints? 19 List the applications of pattern mining? 20 Define Colossal patterns? 21 Consider the 5 transactions given below in table. If minimum support is 30% and minimum confidence is 80%, determine the frequent item sets and association rules using the apriori algorithm. Transaction T1 T2 T3 T4 T5 1 State classification? 2 Define regression analysis? 3 Name the steps in data classification? 4 Define training tuple? 5 Describe accuracy of a classifier? Items Bread, Jelly, Butter Bread, Butter Bread, Milk, Butter Coke, Bread Coke, Milk 6 Differentiate supervised learning and unsupervised learning? 7 Explain the decision tree? 8 Define information gain? 9 State gain ratio? 10 State Gini index? 11 Explain tree pruning? 12 Define the construction of naïve Bayesian classification? 13 Explain the IF-THEN rules for classification? 14 Summarize the size ordering, rule ordering? 15 Describe the class-based ordering and rule-based ordering? 16 Define the rule Pruning? 17 Explain the working of backpropagation?
18 Classify the support vector machines (SVMs)? 19 Demonstrate about the lazy learners? 20 Summarize backpropagation algorithm? 1 Define Clustering? 2 Illustrate the meaning of cluster analysis? 3 Explain the fields in which clustering techniques are used? 4 List out the requirements of cluster analysis? 5 Express the different types of data used for cluster analysis? 6 State interval scaled variables? 7 Define Binary variables? And what are the two types of binary variables? 8 Define nominal, ordinal and ratio scaled variables? 9 Illustrate mean by partitioning method? 10 Define CLARA and CLARANS? 11 State hierarchical method? 12 Differentiate agglomerative and divisive hierarchical clustering? 13 Define Constraint based Clustering? 14 State CURE? 15 Define Density based method? 16 Define DBSCAN? 17 Explain briefly on Grid Based Method? 18 Define STING? 19 Define Wave Cluster? 20 Define Chameleon method? 21 List the basic methodologies for stream data processing and querying? 22 Explain the time series database? 23 Define sequential pattern mining? 24 Explain Constraint based mining of sequential patterns? 25 Explain the applications of time-series databases? 26 Define GSP? 27 Define SPADE? 28 Define Prefix span algorithm? 29 Define SPADE algorithm? 30 List the applications of multiple sequences? 31 Define GSP algorithm? 32 Define prefix span algorithm? 33 Define social network? 34 Explain the qualities that can we look at when characterizing social networks? 35 Define Link Mining? 36 List and Explain the challenges involved in implementing link mining tasks? 37 State multi relational Data mining? 38 Define graph mining?
39 Summarize the purpose of tuple ID propagation? 40 Define Apriori-based approach? 41 Explain in brief about the multi dimensional analysis? 42 List the structured data? 43 Define class composition hierarchy? 44 Summarize the generalization of spatial data? 45 Summarize the generalization of multimedia data? 46 Define spatial data mining? 47 List the applications of spatial data mining? 48 Name the different types of dimensions in spatial data cube? 49 Define Spatial OLAP (SOLAP)? 50 Explain about the multimedia database? 51 State description definition language? 52 Explain the importance of text databases? 53 Illustrate the tasks of text mining? 54 Define latent semantic indexing (LSI)? 55 Define probabilistic latent semantic indexing(plsi)? 56 Define locality preserving indexing(lpi)? 57 Label the basic structure of a web page? 58 Interpret the properties of web linkage structures? 59 Define block-to-page relationships? 60 State page-to-block relationships? 61 Summarize about the financial data analysis of data mining applications? 62 Describe a short note on retail industry application? 63 State about the telecommunication industry application of data mining? 64 List the challenges about the emerging scientific applications of data mining? 65 Describe shortly about the intrusion detection of data mining? 66 List the features based on which data mining systems are assessed? 67 State the theories included in data mining? 68 Express a short note on statistical data mining techniques? 69 Define data visualization? 70 State audio data mining? 71 Interpret the visual data mining? 72 Explain how these principles protect customers from companies that collect personal client data? 73 Describe briefly on counter terrorism application area for data mining? 74 State web-wide tracking? 75 List the principles included in fair information practices? 76 Explain a short note on multi level security? 77 Define encryption? 78 Define intrusion detection? 79 Define security multiparty computation?
80 Define data obscuration? PART B (Long Answer Questions) 1 Describe 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 evolutions of data base technology lead to data mining. d) Describe the steps involved in data mining when viewed as a process of knowledge discovery 2 Distinguish between the data warehouse and databases? How they are similar? 3 Distinguish between the data warehouses and data mining? 4 Explain the difference between discrimination and classification? Between characterization and clustering? Between classification and prediction? For each of these pairs of tasks, how are they similar? 5 Describe three challenges to data mining regarding data mining methodology and user interaction issues? 6 Describe the major research challenges of data mining in one specific application domain, such as stream/sensor data analysis, spatiotemporal data analysis, or bio informatics? 7 Discuss briefly about the data smoothing techniques? 8 Explain the major challenges of mining a huge amount of data (e.g. billions of tuples) in comparison with mining a small amount of data(e.g. data set of a few hundred tuple)? 9 Differentiate operational database systems and data warehousing? 10 Discuss briefly about the data warehouse architecture? 11 Demonstrate the efficient processing of OLAP queries? 12 Explain data mining as a step in the process of knowledge discovery? 13 Describe briefly the concept hierarchy generation for numerical data? 14 Discuss about the concept hierarchy generation for categorical data? 15 Describe the various data reduction techniques? 16 Define data cleaning? Express the different techniques for handling missing values? 17 Discuss issues to consider during data integration? 18 Explain about the various data smoothing techniques? 19 Differentiate between descriptive and predictive data mining? 20 List and describe the five primitives for specifying a data mining task? 1 Differentiate operational database systems and data warehousing? 2 Discuss briefly about the multidimensional data models? 3 Explain with an example the different schemas for multidimensional databases? 4 Demonstrate the four major types of concept hierarchies are schema hierarchies,set-grouping hierarchies, operation-derived hierarchies and rule-based hierarchies.briefly define each type of hierarchy? 5 Describe the three-tier data warehousing architecture?
6 Discuss the efficient processing of OLAP queries? 7 Explain the data warehouse applications? 8 Explain the architecture for on-line analytical mining? 9 Illustrate the applications of data mining? 10 Explain the efficient methods for data cube computation? 11 Describe the common techniques are used in ROLAP and MOLAP? 12 Explain how to compute iceberg cubes by using BUC and star-cubing algorithms? 13 Discuss the complex iceberg condition to compute cube? 14 Explain the discovery-driven exploration of data cubes? 15 Describe the complex aggregation at multiple granularity? 16 Explain about the concept description? And what are the differences between concept description in large databases and OLAP? 17 State and explain algorithm for attribute-oriented induction? 18 Describe mining class comparisons and class description? 19 Compare the schemas for the multidimensional data models? 20 Explain about the data warehouse implementation with an example? 1 Define the terms frequent itemsets, closed itemsets and association rules? 2 Discuss which algorithm is an influential algorithm for mining frequent itemsets for boolean association rules? Explain with an example? 3 Describe the different techniques to improve the efficiency of Apriori? Explain? 4 Discuss the FP-growth algorithm? Explain with an example? 5 Explain how to mine the frequent itemsets using vertical data format? 6 Discuss about mining multilevel association rules from transaction databases in detail? 7 Explain how to mine the multidimensional association rules from relational databases and data warehouses? 8 Describe briefly about the different correlation measures in association analysis? 9 Discuss about constraint-based association mining? 10 Explain the Apriori algorithm with example? 11 Discuss the generating association rules from frequent itemsets. 12 Discuss about mining multilevel association rules from transaction databases in detail? 13 Describe multidimensional association rules using static Discretization? 14 Explain what are additional rule constraints to guide mining? 15 Summarize the usefulness of meta rules in data mining? 16 Explain, how can we tell which strong association rules are really interesting? Explain with an example? 17 Discuss about the correlation analysis based on lift measure? 18 Describe about the correlation analysis using Chi-square? 19 Illustrate about the correlation analysis using All-certainty Measure? 20 Explain about the anti-monotonic rule constraint? 21 The apriori algorithm : Finding Frequent Itemsets using candidate generation.solve the problem by 1) join step 2) prune step
TiD List of Items IDs T100 i1,i2,i5 T200 i2,i4 T300 i2,i3 T400 i1,i2,i4 T500 i1,i3 T600 i2,i3 T700 i1,i3 T800 i1,i2,i3,i5 T900 i1,i2,i3 1 Explain about the classification and prediction? Example with an example? 2 Discuss about basic decision tree induction algorithm? 3 Explain briefly various measures associated with attribute selection? 4 Summarize how does tree pruning work? What are some enhancements to basic decision tree induction? 5 Explain how scalable is decision tree induction? Explain? 6 Describe the working procedures of simple Bayesian classifier? 7 Discuss the backpropagation algorithm and explain? 8 Discuss about k-nearest neighbor classifier and case-based reasoning? 9 Explain about classifier accuracy? Explain the process of measuring the accuracy of a classifier? 10 Describe any ideas can be applied to any association rule mining be applied to classification? 11 Explain briefly about the ensemble methods? 12 Explain about the major issues regarding classifications and predictions? 13 Differentiate classification and prediction methods? 14 Explain briefly various measures associated with attribute selection? 15 Explain training of Bayesian belief networks? 16 Discuss in brief about two conflict resolution strategies? 17 Explain about IF_THEN rules used for classification with an example? 18 Discuss the process of extracting IF-THEN rules using sequential covering algorithm. 19 Explain the different measures used for evaluating rule quality? 20 Explain how SVMs are used for classifying linearly separable data? 1 Discuss the various types of data in cluster analysis? 2 Explain the categories of major clustering methods? 3 Write algorithms for k-means and k-medoids? Explain? 4 Describe the different types of hierarchical methods? 5 Demonstrate about the following hierarchical methods a)birch b)chamelon 6 Discuss about the DBSCAN density-based methods? 7 Explain about grid-based methods? 8 Describe the mode-based methods? 9 Explain the working of CLIQUE algorithm?
10 Explain about semi-supervised cluster analysis? 11 Explain about the outlier analysis? 12 Define the distance-based outlier? Illustrate the efficient algorithms for mining distance-based algorithm? 13 Explain about the Statistical-based outlier detection? 14 Describe about the distance-based outlier detection? 15 Discuss about the density-based outlier detection? 16 Demonstrate about the deviation-based outlier detection techniques? 17 Discuss about the OPTICS density-based methods? 18 Discuss about the DENCLUE density-based methods? 19 Demonstrate about the ROCK(Robust Clustering using links) hierarchical methods? 20 Explain about the agglomerative and divisive hierarchical methods? 21 Explain about the mining of data streams? 22 Discuss the four major components of trend analysis for characterizing time series data? 23 Demonstrate about the similarity search in time series analysis? 24 Describe about the sequential pattern mining? 25 Discuss briefly about the Generalized Sequential Pattern algorithm of scalable method for mining sequential patterns? 26 Explain constraint-based mining of sequential patterns? 27 Explain periodicity analysis? 28 Explain in detail about the alignment of biological sequences? 29 Discuss briefly about the Prefix Span algorithm of scalable method for mining sequential patterns? 30 Discuss briefly about the SPADE algorithm of scalable method for mining sequential patterns? 31 Explain the Apriori-based approach for mining frequent sub graphs? 32 Explain the Pattern-growth approach for mining frequent sub graphs? 33 Describe the characteristics of social networks? 34 List the tasks and challenges of link mining? 35 Discuss about the various areas of mining on social networks? 36 Define multi relational classification? and also explain this classification with Inductive Logic Programming? 37 Explain about the tuple ID propagation? 38 Explain the concept of multirelational classification by using tuple ID propagation? 39 Explain the concept of multirelational clustering with User guidance? 40 Describe about community mining from multirelational networks? 41 Summarize the multidimensional analysis of complex data objects? 42 Summarize the descriptive mining of complex data objects? 43 Discuss briefly about the generalization of structured data? 44 Define class composition hierarchy? Explain how it is generalized by giving a suitable example? 45 Explain the construction and mining of object cubes? 46 Describe the generalization-based mining of plan databases by divideand-conquer with an example?
47 Explain the construction of spatial data cube with suitable example? 48 Describe multimedia databases? 49 Explain mining multimedia databases? 50 Explain briefly about the text data analysis and information retrieval? 51 Describe about the Latent Semantic Indexing(LSI)? 52 Discuss about the Probabilistic Latent Semantic Indexing (PLSI)? 53 Explain about the Locality Preserving Indexing (LPI)? 54 Explain about the keyword-based association analysis? 55 Describe web usage mining? 56 Explain about mining the world wide web? 57 Describe about the mining the web page layout structure? 58 Explain about the aggregation and approximation in spatial and multimedia data generalization? 59 Describe the construction of a multilayered web information base? 60 Express how can be automated document classification be performed? 61 Discuss in detail the application of data mining for financial data analysis? 62 Discuss the application of data mining in business or retail industry? 63 Describe in detail applications of data mining for biomedical and DNA data analysis? 64 Describe in detail applications of data mining for telecommunication industry? 65 Explain how do you choose a data mining system? 66 Illustrate about the data mining in other scientific applications? 67 Demonstrate about the data mining for intrusion detection? 68 Explain the examples of commercial data mining systems? 69 Explain in briefly on statistical data mining? 70 Explain about the visual and audio data mining? 71 Illustrate, is data mining hype or a persistent, steadily growing business? 72 Illustrate, is data mining merely a managers business or everyone s business? 73 Illustrate, is data mining a threat to privacy and data security? 74 Describe about the trends in data mining? 75 Explain briefly about the counter terrorism application area for data mining? 76 Illustrate what can be done to secure the privacy of individuals while collecting and mining data? 77 Explain briefly the privacy enhanced data mining application area? 78 Explain in brief about the ubiquitous and invisible data mining? 79 Describe about the web-wide tracking? 80 Discuss briefly about collaborative filtering approach?
PART C (Problem Solving and Critical Thinking Questions) 1 Suppose that the data for analysis includes the attribute age. The age values for the data tuples are (in increasing order) 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70. compute the following: (a)mean of the data? median? (b) mode of the data? Comment on the data s modality (i.e., bimodal, trimodal, etc.). (c) midrange of the data? (d) Can you find (roughly) the first quartile (Q1) and the third quartile (Q3) of the data? (e) Give the five-number summary of the data. (f) Show a boxplot of the data. (g) How is a quantile-quantile plot different from a quantile plot? 2 Use the data for age given above answer the following. (a) Use smoothing by bin means to smooth the above data, using a bin depth of 3. Illustrate your steps. Comment on the effect of this technique for the given data. (b) How might you determine outliers in the data? (c) What other methods are there for data smoothing? 3 Suppose a hospital tested the age and body fat data for 18 randomly selected adults with the following result age 23 23 27 27 39 41 47 49 50 %fat 9.5 26.5 7.8 17.8 31.4 25.9 27.4 27.2 31.2 age 52 54 54 56 57 58 58 60 61 %fat 34.6 42.5 28.8 33.4 30.2 34.1 32.9 41.2 35.7 Calculate (a) the mean, median and standard deviation of age and %fat. (b) Draw the box plots for age and %fat. (c) Draw a scatter plot and a q-q plot based on these two variables. 4 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 flow in-and-out like possibly infinite 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 efficiently. 5 Suppose that the values for a given set of data are grouped into intervals. The intervals and corresponding frequencies are as follows. Age frequency 1-5 200 5-15 450 15-20 300 20-50 1500 50-80 700 80-110 44 Compute an approximate median value for the data.
1 State why, for the integration of multiple heterogeneous information sources, many companies in industry prefer the update-driven approach (which constructs and uses data warehouses), rather than the querydriven approach (which applies wrappers and integrators). Describe situations where the query-driven approach is preferable over the update-driven approach. 2 Analyze that a data warehouse consists of the three dimensions time, doctor, and patient, and the two measures count and charge, where charge is the fee that a doctor charges a patient for a visit. (a) Enumerate three classes of schemas that are popularly used for modeling data warehouses. (b) Draw a schema diagram for the above data warehouse using one of the schema classes listed in (a). (c) Starting with the base cuboid [day, doctor, patient], what specific OLAP operations should be performed in order to list the total fee collected by each doctor in 2004? (d) To obtain the same list, write an SQL query assuming the data is stored in a relational database with the schema fee (day, month, year, doctor, hospital, patient, count, charge). 3 Suppose that a data warehouse for Big University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg grade. When at the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg grade measure stores the actual course grade of the student. At higher conceptual levels, avg grade stores the average grade for the given combination. Compute the number of cuboids (a) Draw a snowflake schema diagram for the data warehouse. (b) Starting with the base cuboid [student, course, semester, instructor], what specific OLAP operations (e.g., roll-up from semester to year ) should one perform in order to list the average grade of CS courses for each Big University student. (c) If each dimension has five levels (including all), such as student < major < status < university < all, how many cuboids will this cube contain (including the base and apex cuboids)? 4 Suppose that a base cuboid has three dimensions A, B, C, with the following number of cells: A = 1, 000, 000, B = 100, and C = 1000. Suppose that each dimension is evenly partitioned into 10 portions for chunking. (a) Assuming each dimension has only one level, draw the complete lattice of the cube. (b) If each cube cell stores one measure with 4 bytes, what is the total size of the computed cube if the cube is dense? (c) State the order for computing the chunks in the cube that requires the least amount of space, and compute the total amount of main memory space required for computing the 2-D planes.
5 Computing a cube of high dimensionality, we encounter the inherent curse of dimensionality problem: there exists a huge number of subsets of combinations of dimensions. (a) Suppose that there are only two base cells, {(a1, a2, a3,..., a100), (a1, a2, b3,..., b100)}, in a 100- dimensional base cuboid. Compute the number of nonempty aggregate cells. Comment on the storage space and time required to compute these cells. (b) Suppose we are to compute an iceberg cube from the above. If the minimum support count in the iceberg condition is two, how many aggregate cells will there be in the iceberg cube? Show the cells. (c) Introducing iceberg cubes will lessen the burden of computing trivial aggregate cells in a data cube. However, even with iceberg cubes, we could still end up having to compute a large number of trivial uninteresting cells (i.e., with small counts). Suppose that a database has 20 tuples that map to (or cover) the two following base cells in a 100-dimensional base cuboid, each with a cell count of 10: {(a1, a2, a3,..., a100) : 10, (a1, a2, b3,..., b100) : 10}. i. Let the minimum support be 10. How many distinct aggregate cells will there be like the following: {(a1, a2, a3, a4,..., a99, ) : 10,..., (a1, a2,, a4,..., a99, a100) : 10,..., (a1, a2, a3,,...,, ) : 10}? ii. If we ignore all the aggregate cells that can be obtained by replacing some constants by s while keeping the same measure value, how many distinct cells are left? What are the cells? 1 The Apriori algorithm uses prior knowledge of subset support properties. Analyze (a) That all nonempty subsets of a frequent itemset must also be frequent. (b) The support of any nonempty subset s 0 of itemset s must be at least as great as the support of s. (c) Given frequent itemset l and subset s of l, prove that the confidence of the rule s 0 (l s 0 ) cannot be more than the confidence of s (l s), where s 0 is a subset of s. (d) A partitioning variation of Apriori subdivides the transactions of a database D into n nonoverlapping partitions. Prove that any itemset that is frequent in D must be frequent in at least one partition of D. 2 Implement three frequent itemset mining algorithms introduced in this chapter: (1) Apriori [AS94], (2) FP-growth [HPY00], and (3) ECLAT [Zak00] (mining using vertical data format), using a programming language that you are familiar with, such as C++ or Java. Compare the performance of each algorithm with various kinds of large data sets. Write a report to analyze the situations (such as data size, data distribution, minimal support threshold setting, and pattern density) where one algorithm may perform better than the others, and state why.
3 Suppose that a large store has a transaction database that is distributed among four locations. Transactions in each component database have the same format, namely Tj : {i1,..., im}, where Tj is a transaction identifier, and ik (1 k m) is the identifier of an item purchased in the transaction. construct an efficient algorithm to mine global association rules (without considering multilevel associations). You may present your algorithm in the form of an outline. Your algorithm should not require shipping all of the data to one site and should not cause excessive network communication overhead. 4 Suppose that frequent itemsets are saved for a large transaction database, DB. Illustrate how to efficiently mine the (global) association rules under the same minimum support threshold if a set of new transactions, denoted as DB, is (incrementally) added in? 5 Most frequent pattern mining algorithms consider only distinct items in a transaction. However, multiple occurrences of an item in the same shopping basket, such as four cakes and three jugs of milk, can be important in transaction data analysis. Analyze how can one mine frequent itemsets efficiently considering multiple occurrences of items? Propose modifications to the wellknown algorithms, such as Apriori and FP-growth, to adapt to such a situation. 1 Illustrate why is tree pruning useful in decision tree induction? Explain the drawback of using a separate set of tuples to evaluate pruning? 2 Given a decision tree, you have the option of (a) converting the decision tree to rules and then pruning the resulting rules, or (b) pruning the decision tree and then converting the pruned tree to rules. Outline advantage does (a) have over (b)? 3 Outline the major ideas of naive Bayesian classification. Explain why is naive Bayesian classification called na ıve? 4 Design an efficient method that performs effective na ıve Bayesian classification over an infinite data stream (i.e., you can scan the data stream only once). If we wanted to discover the evolution of such classification schemes (e.g., comparing the classification scheme at this moment with earlier schemes, such as one from a week ago), Construct modified design would you suggest? 5 The support vector machine (SVM) is a highly accurate classification method. However, SVM classifiers suffer from slow processing when training with a large set of data tuples. Conclude how to overcome this difficulty and develop a scalable SVM algorithm for efficient SVM classification in large datasets. 1 Given the following measurements for the variable age: 18, 22, 25, 42, 28, 43, 33, 35, 56, 28, standardize the variable by the following: Compute (a) The mean absolute deviation of age. (b)the z-score for the first four measurements. 2 Given two objects represented by the tuples (22, 1, 42, 10) and (20, 0, 36, 8): Compute (a) The Euclidean distance between the two objects. (b)the Manhattan distance between the two objects. (c) The Minkowski distance between the two objects, using p = 3.
3 Suppose that the data mining task is to cluster the following eight points (with (x, y) representing location) into three clusters. A1(2, 10), A2(2, 5), A3(8, 4), B1(5, 8), B2(7, 5), B3(6, 4), C1(1, 2), C2(4, 9). The distance function is Euclidean distance. Suppose initially we assign A1, B1, and C1 as the center of each cluster, respectively. Use the k-means algorithm to show only (a) The three cluster centers after the first round of execution and (b) The final three clusters 4 Explain why is it that BIRCH encounters difficulties in finding clusters of arbitrary shape but OPTICS does not? Can you propose some modifications to BIRCH to help it find clusters of arbitrary shape? 5 Clustering has been popularly recognized as an important data mining task with broad applications. Show one application example for each of the following cases: (a) An application that takes clustering as a major data mining function (b) An application that takes clustering as a preprocessing tool for data preparation for other data mining tasks Explain in stream data analysis, we are often interested in only the nontrivial or exceptionally large cube cells. These can be formulated as iceberg conditions. Thus, it may seem that the iceberg cube is a likely 1 model for stream cube architecture. Unfortunately, this is not the case because iceberg cubes cannot accommodate the incremental updates required due to the constant arrival of new data. Explain why decision tree induction may not be a suitable method for such dynamically changing data sets.a classification model may change dynamically along with the changes of training data streams. 2 This is known as concept drift. Is naive Bayesian a better method on such data sets? Comparing with the naive Bayesian approach, is lazy evaluation (such as the k-nearest neighbor approach) even better? Suppose that a power station stores data regarding power consumption levels by time and by region, in addition to power usage information per customer in each region. Solve the following problems in such a time-series database. 3 (a) Find similar power consumption curve fragments for a given region on Fridays. (b) Every time a power consumption curve rises sharply, what may happen within the next 20 minutes? Construct an efficient mechanism so that regression analysis can be performed efficiently in multidimensional space. regression is commonly used in trend analysis for time-series data sets. An item in a time-series database is usually associated with properties in multidimensional space. For example, an electric power consumer may 4 be associated with consumer location, category, and time of usage (weekdays vs. weekends). In such a multidimensional space, it is often necessary to perform regression analysis in an OLAP manner (i.e., drilling and rolling along any dimension combinations that a user desires).
Suppose that a restaurant chain would like to mine customers consumption behavior relating to major sport events, such as Every time there is a major sport event on TV, the sales of Kentucky Fried Chicken will go up 20% one hour before the match. (a)for this problem, there are multiple sequences (each corresponding to one restaurant in the chain). However, each sequence is long and contains 5 multiple occurrences of a (sequential) pattern. Thus this problem is different from the setting of sequential pattern mining problem discussed in this chapter. Analyze what the differences are in the two problem definitions and how such differences may influence the development of mining algorithms.develop a method for finding such patterns efficiently. 1 An object cube can be constructed by generalization of an objectoriented or object-relational database into relatively structured data before performing multidimensional analysis. Because a set of complex data objects or properties can be generalized in multiple directions and thus derive multiple generalized features, such generalization may lead to a high-dimensional, but rather sparse (generalized) object cube. Explain how to perform effective online analytical processing in such an object cube. 2 A heterogeneous database system consists of multiple database systems that are defined independently, but that need to exchange and transform information among themselves and answer local and global queries. Explain how to process a descriptive mining query in such a system using a generalization-based approach. 3 Outline a scalable method that may effectively preform such generalized structure mining.a plan database consists of a set of action sequences, such as legs of connecting flights, which can be generalized to find generalized sequence plans. Similarly, a structure database may consist of a set of structures, such as trees or graphs, which may also be generalized to find generalized structures. 4 Suppose that a city transportation department would like to perform data analysis on highway traffic for the planning of highway construction based on the city traffic data collected at different hours every day. (a) Construct a spatial data warehouse that stores the highway traffic information so that people can easily see the average and peak time traffic flow by highway, by time of day, and by weekdays, and the traffic situation when a major accident occurs. (b) Outline information can we mine from such a spatial data warehouse to help city planners? 5. An e-mail database is a database that stores a large number of electronic mail (e-mail) messages. It can be viewed as a semistructured database consisting mainly of text data. Explain the following. (a)an e-mail database be structured so as to facilitate multidimensional search, such as by sender, by receiver, by subject, and by time? (b) outline what can be mined from such an e-mail database?
1 Suppose that you are in the market to purchase a data mining system. (a) Regarding the coupling of a data mining system with a database and/or data warehouse system, Distinguish between no coupling, loose coupling, semi-tight coupling, and tight coupling? (b)distinguish between row scalability and column scalability? 2 Study an existing commercial data mining system. Outline the major features of such a system from a multiple dimensional point of view, including data types handled, architecture of the system, data sources, data mining functions, data mining methodologies, coupling with database or data warehouse systems, scalability, visualization tools, and graphical user interfaces. Show one improvement to such a system and outline how to realize it? 3 Outline the major features of such a system from a multiple dimensional point of view, including data types handled, architecture of the system, data sources, data mining functions, data mining methodologies, coupling with database or data warehouse systems, scalability, visualization tools, and graphical user interfaces? 4 Suppose that your local bank has a data mining system. The bank has been studying your debit card usage patterns. Noticing that you make many transactions at home renovation stores, the bank decides to contact you, offering information regarding their special loans for home improvements. Show (a) Discuss how this may conflict with your right to privacy. (b) Describe another situation where you feel that data mining can infringe on your privacy. 5 Illustrate one data mining research issue that, in your view, may have a strong impact on the market and on society. Discuss how to approach such a research issue.explain the major challenges faced in bringing data mining research to market?