Marsden, A. Allen, Douglas R. Kulakowski , F. Kothari, I. Woodson, James R. Cover, Joy A. Hayt Jr. Crowe, Donald F. Elger, John A. Roberson and Barbara C. Meriam, L. Reitz, Frederick J. Alexander M. Moran, Howard N. Munson, Donald F. Young, Theodore H. Okiishi, Wade W. Incropera D. Petrucci; William S. Lang and G. Sepe, W.
Milic and Z. Bertsekas and John N. Friedberg , Arnold J. Insel , Lawrence E. Callister, Jr. Simon , Lawrence E. Schulz, Ajit D. Ahuja , Thomas L. Magnanti , James B. Steven C. I 6th Ed. We will incrementally add answers to those questions in the next several months and release the new versions of updated solution manual in the subsequent months. We sincerely express our thanks to all the teaching assistants and participating students who have worked with us to make and improve the solutions to the questions.
Chai, Meloney H. Chang, James W. Herdy, Jason W. For the solution manual of the second edition of the book, we would like to thank Ph. Their answers to the class assignments have contributed to the advancement of this solution manual. For the solution manual of the third edition of the book, we would like to thank Ph.
Contents 1 Introduction 1. What is data mining? In your answer, address the following: a Is it another hype? Do you think that data mining is also the result of the evolution of machine learning research? Can you present such views based on the historical progress of this discipline? 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. Data mining is more than a simple transformation of technology developed from databases, statistics, and machine learning.
Instead, data mining involves an integration, rather than a simple transformation, of techniques from multiple disciplines such as database technology, statistics, machine learning, high-performance computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial data analysis.
Hence, data mining began its development out of this necessity. Incremental updating Which implementation techniques do you prefer, and why? A ROLAP technique for implementing a multiple dimensional view consists of intermediate servers that stand in between a relational back-end server and client front-end tools, thereby using a relational or extended-relational DBMS to store and manage warehouse data, and OLAP middleware to support missing pieces.
A MOLAP implementation technique consists of servers, which support multidimensional views of data through array-based multidimensional storage engines that map multidimensional views directly to data cube array structures. The fact tables can store aggregated data and the data at the abstraction levels indicated by the join keys in the schema for the given data cube. Roll-up ROLAP: To roll-up on a dimension using the summary fact table, we look for the record in the table that contains a generalization on the desired dimension.
MOLAP: To perform a roll-up in a data cube, simply climb up the concept hierarchy for the desired dimension. For example, one could roll-up on the location dimension from city to country, which is more general.
Drill-down ROLAP: To drill-down on a dimension using the summary fact table, we look for the record in the table that contains a generalization on the desired dimension.
MOLAP: To perform a drill-down in a data cube, simply step down the concept hierarchy for the desired dimension. For example, one could drill-down on the date dimension from month to day in order to group the data by day rather than by month. Incremental updating.
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