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Did You Know This About Data Mining?
Data mining is one of the emerging topics in database management systems. With the help of data mining, you can examine a pre-existing database to generate new information. Sometimes students face difficulties in making the accurate solution for data mining homework or assignment. Hence they become unable to get high scores in exams.
Data Mining is a part of computer science, from where we determine new information from the existing data set. Data mining is required to determine anomalies, patterns, and correlations in a large data set, and with the help of these data, we can predict the outcomes. So we are providing you with the best Data Mining Assignment Help, Data Mining Homework Help, Data Mining Homework Assignment, and Data Mining Coursework at Dream Assignment.
Father of Data Mining
The Non-Executive Director of Houston Analytics named Colin Shearer is known as the father of data mining. Previously he had a great career in IBM, then left and join with his partners to focus on his passion.
Types of Data Mining:
Data mining can be performed on various types of databases they have included:
● Text databases
● Object-oriented databases
● Object-relational databases
● Data warehouses
● Spatial databases
● Streaming and multimedia database, etc.
There are mainly two types of tasks that are performed with the help of data mining, the first is describing the general properties of existing data and the second one is predicting data outcomes.
5 Steps in Data Mining Process
There are five steps of the data mining process including:
○ Identifying the source information
○ Choosing the data that need to be analyzed
○ Extracting the relevant information
○ Identifying the key values from the extracted data set
○ Interpreting and reporting outcomes.
`Identify Source Information
At the beginning of the data mining method, you need to investigate different data models and different datasets and then merge them to establish the required dataset. Identifying information sources is important to determine which data set you should be analyzing to retrieve information.
Choosing Data
In a huge data set, there are different types of data that exist but all of those are not required for analysis, so we need to pick specific types of data. There are massive uses of Bayesian data analysis to choose relevant data from an existing dataset. When someone gets a failure to choose the proper data then they also fail to execute the required information from the existing data set.
Extracting the Relevant Information
After deciding which type of data is required from the existing dataset, we need to extract data for further analysis. We needed to transform data as per requirement and this transformation included several steps such as smoothing, aggregation, generalization, normalization, and attribute construction.
Identify Key Values
Identifying key values is the next step of data mining, this step is important because if anyone chose some uncertain value as a key-value then the required outcomes changed to the other outcomes.
Interpreting and Recording Outcomes
It is the final stage out of the five stages, it includes resolving the information in more qualifiable values by using basic numerical counts, group comparison, and direct value comparison to determine specific elements.
The last stage contained modeling, evaluation, and deployment. In modeling, there are some mathematical models that are used to determine patterns of data. The next step is evaluation, in this step identified patterns are evaluated against the objectives. In the last step called deployment, we need to ship our data mining outcomes to regular operations.
Data Mining Techniques
There are a total of seven techniques of data mining, they are included
● Classification
● Clustering
● Regression
○ Linear regression
○ Non-linear regression
○ Logistic regression
● Association Rules
● Other detection
● Sequential patterns
● Prediction
Advantages of Data Mining
●Assist to get knowledge-based information
● Provide profitable adjustments in production and operation.
● Help in the process of decision making
● Quick process of analyzing large amounts of databases easily.
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