Data mining

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Running head: DATA MINING 1

DATA MINING 2

Data Mining Name

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Data Mining

Data mining is a process of identifying valuable hidden information by analyzing data of large amounts, which is kept in data warehouse or databases, using

various techniques such as artificial intelligence, machine learning, and statistical methods (Tan, Steinbach & Kumar, 2016). It is a process that is applied by companies and industries to convert raw data into meaningful information, which can be useful in enhancing the processes of the industry. Businesses can use software to check for patterns in large amounts of data to get more information about their customers, enhance their processes, increase sales, create more effective strategies for marketing and reduce costs involved in their operations. The auto industry is a manufacturing industry that deals with the production and selling of motor vehicles. The automotive industry comprises of various manufacturing organizations and companies that design, develop, manufacture, market, and sell motor vehicles, motorcycles, moped, and towed vehicles. The auto industry combines a number of different steps that assist them in coming up with car units and also marketing the

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products. The last step involved in the auto industry is the marketing of the products they make, such as motor vehicles and cars. Data mining can be applied in all steps involved in the manufacturing processes of the automotive industry (Aggarwal, 2015). Marketing of the products in the auto industry can be enhanced by subjecting it to data mining. Data mining can be an important tool in enhancing marketing in companies, industries, and organizations. The application of data mining in the marketing process can be used to identify useful information about customers, demand, customer behaviors, and markets for auto industry products. This research paper will study the application of data mining in the marketing stage in the automotive industry. There are various processes involved in the use of data in marketing. The process involves the selection of data, data pre-processing, data transformation, data mining, and evaluation/interpretation of data. Data mining

in the marketing step of the auto industry takes six phases. The phases understand the business, data understanding, data preparation, evaluation, modeling,

and deployment. To enhance marketing in the automotive industry, data mining can be applied using the following techniques. Analysis of clusters to identify each target groups Cluster analysis helps in identifying a specific customer group based on common characteristics within the customer database (García, Luengo & Herrera, 2015). The common customer features could be age, gender, education level, geographic location, religion, social class or status, and societal beliefs. These customer characteristics help companies to get useful information that can be used to make marketing decisions. This technique of data mining in marketing in the automotive industry is essential in segmenting or clustering the customer database. The clustering helps in making valuable marketing decisions that assist in making production and marketing decisions in the auto industry. For example, cluster analysis will help a car selling company that sends promotions to the relevant target group of customers for the products. Preferences, tastes, and choices of purchasing automotive products differ from one group of customers to another. For instance, the type of cars which youths like to purchase is different from cars aged people like to drive. Therefore data clustering and analysis in the marketing of auto products help to know which groups to target while producing, marketing, and to sell the products. Regression analysis to assist in marketing forecasts and predictions Companies and organizations need to understand the future of the markets for their products. Marketing predictions and forecasts involve foreseeing the future trends in customer behavior, demand, supply from rival manufacturers, and other things that can affect the sales of the company. Data mining provides regression analysis techniques that can enable marketing personnel in the auto industry to study habits, changes, satisfaction level of customers, change in tastes and preferences, advertisement, and cots involved (Dholakia & Dholakia, 2015). In the marketing of auto industry products, the customer and market data available can be used to conduct regression analysis for predicting or forecasting future changes in markets for the products. For example, a car manufacturer can use data mining through regression analysis to predict how a certain car model will sell in the market in the future. Conducting anomaly detection to identify abnormalities The auto industry and the relevant companies have to deal with the consequences of misconduct and mistakes that are committed by suppliers, employees, or customers. A small mistake committed in product purchase or data entry can be very bothersome to the company.

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Companies need to do way with any mistakes committed in the marketing process. The auto industry is not an exception in this case. In the marketing of the products of this industry, some errors or mistakes could be made. To eradicate any occurring database anomalies, inconsistencies, errors, or mistakes, data mining techniques are used. The data mining technique used is called anomaly detection. Marketing records can be subjected to data mining software to detect and correct any

anomalies. This can help the auto industry to evade marketing errors. Intrusion detection to achieve greater security for the marketing systems Like any other company, auto companies need to protect their marketing data from intrusion to avoid contamination of data. To evade using databases that are contaminated by intruders such as hackers or viruses, marketing departments can detect or search for intruders by doing data mining. This data mining technique is essential in decontaminating the marketing database and providing greater data security in the entire system (Hofmann, Neukart & Bäck, 2017). Summarization of market data for the auto industry Marketing processes involve many activities such as advertisement, promotion, transportation, hiring of the sales force, and many more activities. All these activities create large amounts of data for the company, for instance, data concerning all customers, all sellers, distributors, promoters, and many others. The automotive companies may not be able to use all these data as it is collected because it is too big. The company, therefore, needs to summarize that data to make it more useful. Data mining can be used in the summarization of these huge amounts of data to more simple, organized, understandable, and useful information (Oliff & Liu, 2017). Decision tree classifiers

A decision tree is a predictive approach for modeling used in data mining, machine learning, and statistics. Decision tree classification is an organized approach

used to build models of classification from a given input set of data. Some of decision tree classifiers include neural networks, rule-based classifiers, na ve Bayes

classifiers, and support vector machines (Parvin, MirnabiBaboli & Alinejad-Rokn, 2015). In conclusion, data mining can be applied in the auto industry to enhance marketing. Data mining involves analysis, classification, and summarization of data into useful and meaningful information (Tan, Steinbach & Kumar, 2016). Data mining can be used to use the clustering of marketing data, detecting errors, and summarizing marketing data to enhance the marketing process of the auto industry. References

Aggarwal, C. C. (2015). Data mining: the textbook. Springer. Dholakia, R. R., & Dholakia, N. (2015). Data mining and marketing. The International Encyclopedia of

Digital Communication and Society, 1-10. García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining (pp. 195-243). Cham, Switzerland: Springer

International Publishing. Hofmann, M., Neukart, F., & Bäck, T. (2017). Artificial intelligence and data science in the automotive industry. arXiv preprint

arXiv:1709.01989. Oliff, H., & Liu, Y. (2017). Towards industry 4.0 utilizing data-mining techniques: a case study on quality improvement. Procedia CIRP, 63, 167.

Parvin, H., MirnabiBaboli, M., & Alinejad-Rokny, H. (2015). Proposing a classifier ensemble framework based on classifier selection and decision tree. Engineering

Applications of Artificial Intelligence, 37, 34-42. Tan, P. N., Steinbach, M., & Kumar, V. (2016). Introduction to data mining. Pearson Education India.

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DATA MINING 1

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2.1 Overview of Data Mining

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[2] Data Mining

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Data Mining Name

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"Data Mining"

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Data mining is a process of identifying valuable hidden information by analyzing data of large amounts, which is kept in data warehouse or databases, using various techniques such as artificial intelligence, machine learning, and statistical methods (Tan, Steinbach & Kumar, 2016).

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In data mining, the organization will give us the opportunity to go through the process of discovering hidden valuable knowledge by analyzing large amounts of data that is stored in databases using the data mining techniques such as machine learning, artificial intelligence and statistical methods

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The process involves the selection of data, data pre-processing, data transformation, data mining, and evaluation/interpretation of data.

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Selection Pre-processing Transformation Data mining Interpretation/evaluation.[5]

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The phases understand the business, data understanding, data preparation, evaluation, modeling, and deployment.

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The different phases are Business Understanding—Data Understanding – Data Preparation – Modeling – Evaluation —Deployment

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The data mining technique used is called anomaly detection.

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To eliminate any database inconsistencies or anomalies at source, a special data mining technique is used called anomaly detection

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Decision tree classifiers

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A Decision Tree

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A decision tree is a predictive approach for modeling used in data mining, machine learning, and statistics.

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Decision tree is a predictive modeling approach used in data mining and general statistical analysis

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Some of decision tree classifiers include neural networks, rule-based classifiers, na ve Bayes classifiers, and support vector machines (Parvin, MirnabiBaboli & Alinejad-Rokn, 2015).

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Examples include decision tree classifiers, neural networks, rule-based classifiers, support vector machines, and naive Bayes classifiers

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Data mining and marketing.

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R and Data Mining

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García, S., Luengo, J., & Herrera, F.

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Luengo J, García-Gil D, Ramírez-Gallego S, García S, Herrera F

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arXiv preprint arXiv:1709.01989.

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arXiv preprint arXiv:1709.09003

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Parvin, H., MirnabiBaboli, M., & Alinejad- Rokny, H.

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PARVIN H, MIRNABIBABOLI M, ALINEJAD- ROKNY H

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Proposing a classifier ensemble framework based on classifier selection and decision tree. Engineering Applications of Artificial Intelligence, 37, 34-42.

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Proposing a classifier ensemble framework based on classifier selection and decision tree[J] Engineering Applications of Artificial Intelligence, 2015, 37:34-42

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N., Steinbach, M., & Kumar, V.

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N., Steinbach, M., & Kumar, V

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Introduction to data mining.

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Introduction to Data Mining

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Pearson Education India.

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Pearson Education, India