Data Mining Techniques Homework Help



  • DATA MINING TECHNIQUES HOMEWORK HELP:
    Data mining techniques are an important part of the study of data mining and often form a major portion of homewor and assignments. https://www.allhomeworkassignments.com/ data mining experts are well-versed in all aspects of data mining, data mining trends and processes and guarantee you best results.
    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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