Bayseian Classifier Implementation

Topics: Data mining, Bayes' theorem, Naive Bayes classifier Pages: 12 (3228 words) Published: June 1, 2013
Ravinder pal Singh Id-6405231

Harsimran Jit Singh Mutti Id-6241905


In data mining, classification is a form of data analysis that can be used to extract models describing important data classes and it predicts categorical class labels and classifies data. There are many algorithms which are used in classification i.e. ID3, C4.5, Apriori, FP-growth, Back propagation Neural Network (BNN) and Naïve Bayes (NB). Bayes data mining technique are a fundamentally important technique. Bayes theorem finds the event occurring probability given the probability of another already occurred event. Bayes Rule is applied for calculating the posterior from the prior and the likelihood, due to the later two is generally easier to be generated from a probability model. Statistics provide a strong fundamental background to quantify and evaluate the results. However, algorithms based on statistics need to be modified and refined before they are applied to data mining. General terms Keywords Data mining, knowledge base, Naive Bayesian Classifier, Bayesian theorem

from the huge amount of data, which can be utilized for future prediction or intelligence and also for knowledge discovery. There are many applications of data mining techniques in various fields such as engineering, medical, financial, and business. Here we have discussed application of classification algorithm which is an important part of the data mining algorithms into medical field.

Motivation and Real world scenario
With the increasing innovation power, data-mining methods helps us to reduce the risks associated with conducting any particular task and improve decision-making. Especially here we have applied data mining techniques to classify a set of patient’s data having heart attacks in the past, so that we can have some useful information from it. The major motivation behind this work is that there is a need to develop systems using advance technologies of computer science to help doctors in making important decisions to save patient’s life in case of a similar case and also if there is a situation in which the patient’s survival is not possible , expenditure of money on medical procedures should be saved. Most developing countries do not have sufficient medical specialists to see the patient and give proper treatment. Patients suffering from various diseases often die even after spending a lot of money on the treatment. This happens because there are not enough medical specialists. The insufficient of medical specialists will never be overcome within a short period of time. In order to achieve such goals, we need to fully exploit technology and data mining is such a tool by which we can achieve some success. In order to exploit technology using data mining we have to prepare the data by extracting all the useful information from it. When data mining algorithms can be used before

Data is generated at a great pace in today’s world. Our ability to generate data outstrips our ability to explore, analyze, and understand it. Data mining is basically in common man’s language a process of knowledge discovery from a given set of data. It is relatively young field of computer science but it can have great impact on our daily world utilities. Data mining techniques give us new power to discover and to change the existing large volume of data. Data mining processes can discover interesting information

that a target dataset has to be assembled. As data mining may only uncover patterns already present in the data, the target dataset has to be large enough to have large no. of patterns while at the same time, remain to be concise enough to be mined in an acceptable...

References: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. gs/id3-c45.html 11/b28129/algo_nb.htm -Fall06/reading/NB.pdf ier 604_chap06.pdf Lecture Notes By Benjamin. C. M. Fung f w/overview.html f/aimj-medkdd1.pdf ram n-tree-learning-acting-as-a-cardiologist
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