MITS5509 Intelligent Systems for Analytics Assignment 1 and 2 Presentation and Research Report MITS5509 Assignment 1 and 2 Copyright © XXXXXXXXXXVIT, All Rights Reserved. 2 NOTE: This Document is used...

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Answer To: MITS5509 Intelligent Systems for Analytics Assignment 1 and 2 Presentation and Research Report...

Dilpreet answered on Dec 19 2021
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CRITICAL ANALYSIS OF DATA MINING TASKS
Table of contents
Introduction    3
Genetic programming framework    3
Description of genetic algorithms and genetic programming systems    4
Discussion of phenotype of genetic programming individual or data mining    5
Implementing genetic programming for the task of classification    5
Implementing genet
ic programming for the task of generalized rule induction    6
Findings    6
Conclusion    6
References    7
Introduction
Data mining can be defined as practices to determine patterns or gain information from a large set of pre existing data. It is analysis of large sets of raw data done through various methods in order to obtain meaningful information. This is used to extract useful knowledge from existing databases which contains voluminous data gathered from the real world. There are five major data mining tasks identified as deviation detection, summarization, classification, generalized rule induction and clustering. The article, which has been selected for critical analysis discusses about genetic programming framework for two major data mining tasks namely classification and generalized rule induction. This genetic programming framework encodes database queries corresponding to high level rules, which are used to predict the values of some of the attributes Freitas [1].
The framework, which has been discussed in this article emphasizes particularly on the integration between relational database systems and the GP algorithm. This integration can prove to be extremely advantageous in case of data mining scalability, automatic parallelization and controls for data privacy. This report is also intended to highlight some genetic operators which have been designed and developed exclusively for data mining tasks such as classification and generalized rule induction. The intersection of machine learning and databases in data mining helps to extract comprehensive knowledge with high degree of autonomy. Data mining procedures significantly improve the efficiency of analyzing data sets to discover useful knowledge. Genetic programming framework is capable of addressing or incorporating all the characteristics of data mining procedures. This report is aimed to highlight the genetic programming framework for classification and generalized rule induction.
Genetic programming framework
Genetic programming framework encodes data base queries and works on algorithms, which are intended to correspond to high level rules. These are then used to predict the values of some of the attributes. This framework is considered suitable for data mining tasks because of its robustness of algorithm and easily understandable structure of rule generation. This operates by submitting SQL queries to database servers helping to achieve integration between genetic programming and relational databases. Genetic programming algorithms or framework are capable of searching many parts of the database or the program simultaneously with high efficiency. These frameworks are more autonomous and thus help to minimize the need for specific domain knowledge. SQL servers automatically parallelize the queries generated through genetic programming frameworks.
Description of genetic algorithms and genetic programming systems
Several genetic algorithms have been developed to support the genetic programming framework. These genetic...
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