TABLE OF CONTENTS INTRODUCTION...........................................................................................................................1 Methods and techniques supporting artificial intelligence and Data Mining..................................1 Comparative analysis of AI and Data Mining.................................................................................4 Literature review on application and concept of AI and Data Mining............................................5 CONCLUSION................................................................................................................................6 REFERENCES................................................................................................................................7
INTRODUCTION Artificial intelligence (AI) is defined as an advanced computer science technology in whichintelligentmachinesaredevelopedwhicharecapabletodemonstratethenatural intelligence, similar to human beings or other living organisms (Pan, 2016). Data mining is a practice to analyse large size of data which is pre exists in the record to generate new data and information. AI is playing significant role in healthcare, manufacturing and in aspects which are beyond human imagination or reach. The Statical techniques are used in the AI to perform the different operation based on the mathematical based solution. The study will analyse the techniques which supports the AI and its applications. It will also provide a comparative evaluation of AI and other recent technologies. The data mining technology is used to analysis the pre exist to generate the new out comes. AI is problem solving technology which is based on the predefined process and information. The AI works on the predefined algorithm and gain the information form data analysing process. Methods and techniques supporting artificial intelligence and Data Mining Data Mining Data mining is a software which is used to examine the pre-existed data to generate the result and find the patterns in the data for better classified result. The various techniques used in the Data Mining are- 1.Association-This technique is to collect the information which are of the same type. In this the data of same nature is scanned and then collected together for more convenience. 2.Classification-It is based on the arranging the data of the same type in the classified manner. This technique scans the data and separate the same type of data together in classified way. This used in the businesses to classify the information. 1
3.Clustering- This technique is used in the production department of the organisation to separate the product as the predefined information. This method uses the physical scanners to take the input and then process the information to perform the action. 4.Prediction-This method is mostly used by the scientists and the business annalist to predict the future result. In this technology the collected data is scanned and the patterns are evaluated. This scanned patterns are used to scan current situation data and predict the result. 5.Sequential Pattern- Thesetechniques are used to find the patterns in the data and judge the current situation. To find the trends and business period. 6.Decision Tree- These techniques are used to generate the decision via the machine learning. In this process the data is analysed and as per the information patterns the technique generate the most suitable result for the data (Aggarwal, 2015). Statistical Techniques Since other techniques have predefined and installed software and algorithm they are not required to use logics or decision at their own. However contrary to this AI devices first try to discover the possible solution of given problem and then it is executed. The techniques which supports AI are largely based upon mathematical and statistical models so that it can show human intelligence. The most popular methods which are used for the development and implementation of AI are as follows: Problem Framing Problem framing of AI based on the designing of AI numbers of nodes are used which are capable identify data patterns. It uses methods like supervised and unsupervised learning, reinforced learning for the pattern recognition (Li & Du, 2017). Neural network techniques help 2
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AI to make decisions on the basis of logical reasoning and identification. This helps the AI to calculate the solving method. Problem Understanding It is machine algorithm which is based on supervised learning method so that problems can be classified and regression challenges can be overcome. The ability of AI to solve problems at their own is achieved by this technique by understanding the problem and its type. For the decision makes by AI are based on this process. This technique is also used by AI for performing functions such as image and text recognition as well as face recognition. Data Cleaning The ability of AI to clean the data which is not useful to solve data problems quickly then this method is provided to keep the AI free from rubbish data. This algorithm aims to reduce the west data so that minimum number of alternatives are present and fast decisions can be made because it needs to process small amount of data. It also assists AI machines to recognise the mistakes and to learn from them so that similar to human being experience and learnings can be continuously increase. As per the above techniques the artificial intelligence use these techniques in collective manner to develop an artificial intelligence device to reduce the work load of human. For example in the large scale organisation the artificial widely used in the production line and assembly line. These devices usually known as industrial robots. This type of robots use both data mining and statical techniques to perform the required action for the productionand assembly. In the mobile assembly the procedure need high precision to make the electrical connection proper for required functioning of cellphone. This is how the artificial intelligence is now used for high precision works. 3
(Source:Introduction to Natural Language Processing. (2018)) Comparative analysis of statistical Technique and Data Mining Artificial Intelligence work is based on the algorithm structure which is designed to analysis the data gain the information and then implement this learning in problem solving. The statistical techniques are the techniques which are used on the base of the interface with the humans and physical experiences and store the information collectively from each operation and further use this information to solve the different task. But when it comes to Data Mining, Data Mining scans the available data and process through different algorithms and gives the unique solution or result for current data related query. The main difference of the Artificial Intelligence and Data Mining is the information screening with collective storing and other is the problem solving skills. Statistical TechniquesData Mining It is the basic method which is consists of theIt is the advance way to scan the information 4 Illustration1: Natural language processing
mathematical formulas and technique to scan and analyse the raw data. and sort it for unique report. The of statistical techniques works with mathematical formulas to calculate the further result. Data mining works on the patterns and sequences of the data to find the result. Statistical Techniques only follows the algorithm to implement the mathematical equation and formulas on the data based on the required action. Data Mining is based on the learning algorithms, which learns from the situation and data to gives the calculated result. Statistical Techniques is process to examine data with the formulas to produce the result. Data Mining analyse the data and store the information in collective manner to solve the problem related to the data. Statistical Techniques process don't have self intelligence it only follows the algorithms and mathematical processes. Data Mining follows the algorithms which are more complex and design to learn. And follow both given commands and self generated commands. It is simple to understand. Less complexity.The complexity is higher in data processing. Literature review on application and concept of Statistical Techniques and Data Mining Contrary to thisLu & et.al., (2018)asserted that there is high need when for assuring the developments we must encourage Statistical Techniques but its reliabilitymust not be neglected. The increasing role of Statistical Techniques is giving the result data as per the formulas this may lead to the different data than the reality. The statistical techniques are based on the mathematical formulas to study the raw data to produce the resultIn such situation in future it may become 5
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impossible to stop or manage Statistical Techniques because they will more powerful than human. As per the views ofTan, P. N. (2018)the data mining is the basic part of the Artificial Intelligence. The techniques of Data Mining are the basic principle for the artificial intelligence. The data processing method used in the Artificial Intelligence is from Data Mining and inspired from it. The same techniques which are used in the data mining are implemented in the AI. The Ai is the advanced version of the data mining with the additional features of advanced decision making and problem solving technique. CONCLUSION The study of Data mining shows that the process is less complex and easy to understand and required machinery is less than the AI. But it has some limitation in the operation. From the above study it can be concluded that statistical techniques are better than Data Mining and can be considered as one of the most critical developed technology which can take the superior decision and can conduct the decision on its own which make more effective cause this are based on the mathematicalsolution.However,thefactcannotbedeniedthatasitisapproachingto applications which are beyond human capabilities gradually it is also creating a threat on humanity and substitution of human beings. 6
REFERENCES Books and Journals Aggarwal, C. C. (2015).Data mining: the textbook. Springer. El Kadiri, S. & et.al., (2016). Current trends on ICT technologies for enterprise information systems.Computers in Industry.79.14-33. Fortunati, L. (2017). The human body: Natural and artificial technology. InMachines that become us(pp. 71-87). Routledge. Li, D., & Du, Y. (2017).Artificial intelligence with uncertainty. CRC press. Lu, H. & et.al., (2018). Brain intelligence: go beyond artificial intelligence.Mobile Networks and Applications.23(2). 368-375. Pan, Y. (2016). Heading toward artificial intelligence 2.0.Engineering.2(4). 409-413. Posada, J. & et.al., (2015). Visual computing as a key enabling technology for industrie 4.0 and industrial internet.IEEE computer graphics and applications.35(2). 26-40. Russell, S. J., & Norvig, P. (2016).Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited, Tan, P. N. (2018).Introduction to data mining. Pearson Education India. Online Fourtane., S., (2018).5 Technology trends to watch in 2019. [Online]. Accessed through <https://interestingengineering.com/5-technology-trends-to-watch-in-2019> Ghosh., P., (2018).The Future of Machine Learning and Artificial Intelligence.[Online]. Accessedthrough<https://www.dataversity.net/future-machine-learning-artificial- intelligence/> 7