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Pattern Tracking: Pattern tracking is one of the most important and fundamental data mining techniques. Here, data scientists and researchers will track patterns of changes or abnormalities in the given data set and use them for the purpose for which it is trakced. Examples of this Data Mining Technique: sales of a certain brand spikes just before winter or sales of a certain product dips just before exams.
Classification: Classification is another important data mining technique where data is classified into certain categories depending upon the kind of information https://www.allhomeworkassignments.com/ are trying to extract. After data is thus classified, data scientists are able to view the pattern or information they are looking for. Example: A company is trying to sell its products to a certain group of customers. Data scientists classify the data according to the income range of the target customers as 'high income', 'medium income' and 'low income' where customers in the 'high income' group are most likely to buy the product.
Association: This is another Important Data Mining Technique where data scientists and researchers look for variables and relations in the data. A good example of this would be trying to find out customers who buy one product also tend to buy a certain other product. Projecting this kind of information to the right audience can actually boost sales of multiple products.
Detection of Outliers: Detection of Outliers is nothing but detection of abnormalities in a given data set. This technique is used when data researchers are not able to detect a clear pattern in the given data set - so they look for abnormalities, also known as 'anomalies'.
Clustering: Clustering is an Important Data Mining Technique where data is classified on the basis of their similarities.
Regression: Regression is a Data Mining Technique which is primarily used for modelling and planning. Here, data scientists look for the presence of absence of any variable, trend or parameter based on the presence or absence of certain other variables.
Prediction: Prediction is an Important Data Mining Technique. Here, data scientists can make predictions based on their study of previous or historical trends. In other words, they try to predict the likelihood of an event or trend based on historical data trends.
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Data Mining in Telecom: Data mining in telecom industry can be used to predict frauds, analyzing trending market situations, interests of the customers as well as what influence a customer to call specifically at certain times. It also helps in profiling as well as customer segmentation. Data Mining In Tele Communication Industry also helps in identifying marketing effectiveness along with network faults.
Data Mining in Education: Optimization data mining techniques in the education sector helps in developing various methods to extract specific type of data that comes from academic sectors. It helps in predicting student involvement by determining student’s t performances in various fields. Added advantage of using Data Mining In Education Sector is that it helps to provide better opportunities for student’s career counselling.
Data Mining in Marketing: Data Mining Helps various marketing companies to build models based on previous records in order to predict what kind of responses will be recorded for campaigns like online marketing campaign, direct mail etc.
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HOW IS DATA MINING USED? WHERE IS DATA MINING USED?
Data Mining in Agriculture: Data Mining In Agriculture can be helpful in predicting crop condition, forecasting market development and evaluating consumer solvency. In short it can help the farmers with prior information as to what time of the year their crops will flourish along the what products are being asked by the consumers more. Agronomists in particular use data mining in measuring the quality as well as growth of the plant.

Data Mining in Health Care: The various healthcare organizations make use of data mining in healthcare. It helps the industry by providing various methodologies and techniques for monitoring treatment effectiveness. It also helps the critical patients through predictive medicines. The health care industry includes medical records, administrative records and reports. Using Data Mining Medical Research, medical devices, pharmaceuticals and hospital management can be taken care of.
Data Mining in Banking: In banking data mining is used to churn out valuable information from the customers distinctively. This strategy is basically based upon cost and benefit analysis in order to monitor bank revenues. Data Mining In Banking Sector is also use to evade credit as well debit card fraudulent cases. In banking business, in order to predict future risks data mining is very much helpful. Analyzing the market to ensure profitability, techniques of data mining are widely used.

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