Solution Manual Data Mining Concepts And Techniques 3rd Edition NEW!

(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 machinelearning?No. Data mining is more than a simple transformation of technology developedfrom databases,statistics, and machine learning. Instead, data mining involves an integration, rather than asimple transformation, of techniques from multiple disciplines such as database technology, statis-tics, machine learning, high-performance computing, pattern recognition, neural networks, datavisualization, 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 databasecreation mech- anisms that led to the development of effective mechanisms for data management including data storage and retrieval, and query and transaction processing. The large number of database sys- tems offering 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.

Solution Manual Data Mining Concepts And Techniques 3rd Edition

Describe whyconcept hierarchiesare useful in data mining. Answer: Concept hierarchies define 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 ina 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 different perspectives, gaining further insight into relationships hidden in the data. Generalizing has the advantage of compressing the data set, and mining on a compressed dataset will require fewer I/O operations. This will be more efficient than mining on a large, uncompressed data set.