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What is Data Mining SQL? Data Mining SQL Tutorial Guide for Beginner

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What is Data Mining?

As per Wikipedia “Data Mining is the process of discovering new patterns from large data sets”. Now for the beginners, the big question is that how it is different from a normal database. In a database, usually the data are stored and accessed and that is not in the case of data mining. Now you may think that what is data mining?

data mining

The database is also a key part of data mining but here “Knowledge Discovery in Database” is the process that is followed in the data mining. This KDD or Knowledge Discovery in Database process can be divided further into five steps:

  • Selection
  • Preprocessing
  • Transformation
  • Data Mining
  • Evaluation

Here, among these steps, every process contributes to the complete data mining cycle. Here ‘Selection’ step involves data selection while pre-processing means cleansing of data, transformation involve data preparation and data mining is performed on this data to produce relevant information which is evaluated later.

Data Mining SQL

Here in this cycle, we have used the word Knowledge in KDD, not data or information as there are differences in these terms. As a result, data mining is not the plain information or data instead this is a result of processing information. Data mining is all about discovering “new” pattern from the existing data and to deal with predictive analysis.

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Parameters for Data Mining

Association rules are created by analyzing data and most important relationships are created to locate important relationships within the data. Here two terms Support and Confidence are used in which Support represents the frequency of data item appearance and confidence means the total number of times if/then statements are used in data processing.

Parameters for Data Mining

Other parameters of data mining are Path Analysis, Sequence, Clustering, Classification, and Forecasting. Here Path Analysis parameter looks for patterns or sequence of events. Sequence signifies an ordered list of items, it is found in many data structures of a database. Classification parameter looks for new patterns and can result in a way in which data is organized. Classification algorithms can predict the variables within the database.

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Clustering parameter cluster or group the documents of the fact that were previously not known. A set of objects are generally grouped in clustering that is then aggregated as per their similarity with each other. Forecasting parameter can discover patterns in data mining that can result in reasonable predictions for future, it is also known as predictive analysis.

Tools and Techniques Used for Data Mining

In many research areas, the techniques of data mining are used that include genetics, cybernetics, mathematics, and marketing. Organizations use data mining techniques to predict customer behavior and to drive efficient result usually. By using predictive analysis techniques businesses can even set them apart from their competitors.

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Web mining technique is usually used in CRM in which information is integrated and gathered in the traditional way by using old data mining techniques by using the web. Main aim or objective of web mining is to understand the customer behavior and to know and evaluate the effectiveness of a particular website.

One another data mining technique is network approaches. This approach is based on multitask learning and is used to ensure parallel and scalable data mining algorithm execution and mining of large databases. Machine learning is also a tool for data mining and is used to design specific algorithms that are used to learn and predict the behavior of data.

Several Phases of Data Mining Development

Several phases are involved in data mining technology and each phase of data mining has a different purpose and is considered for that specific purpose only.

Several Phases of Data Mining Development

Below are listed the phases of the complete process:

Problem Definition

Problem definition is about defining the problem for which you are using data mining and you must also know what type of relationship you want to get through data mining. Your problem must reflect your business policies and processes. You must know that whether you are using data model for any prediction or to get any association or pattern among data sets?

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In case of prediction, you must know that about which attribute you want to know? Table relationship must be specified and you must know that whether you need any aggregation, processing, and cleansing to make data usable?

Data Preparation

Data can be unstructured and scattered so you must know that where data is located exactly and in which format? If there are any inconsistent or missing entry then you should priory know about it. Data cleaning does not refer to deletion of any inappropriate data or to populate the missing values. Data cleansing mean to establish a relationship between data values and to identify the exact data source. In case of tables, you must know that which column should be used. You can also use data preparation tools here that can be SQL Server Integration Service, Data Quality Service or Master Data Service.

Data Exploration

At the time of mining model, you must have the idea of your data. The techniques that are used for exploration are minimum and maximum value calculation, standard deviation calculation and to look for data distribution. It can provide stability information and accuracy of the result. Here, the large standard deviation signifies that you must use more data to improve the data model.

Data Mining Model Development

Columns for mining structure must be specified. This structure must remain empty until processed data filled the structure. On mining structure processing statistical information is generated that can fill the tables. Model processing is also known as training that is about applying specific mathematical algorithms to the data to identify exact patterns.

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Patterns that are found in training depends on data selection for the training process. Many different algorithms are used in SQL Server 2016 that can create a different model. Data Mining Wizard can also be used to create any specific and pre-defined data model.

Data Mining Applications

Today, data mining techniques are being used by many organizations and have a vast area of application. Following domains are mainly using this technique:

  • Market Analysis and Management
  • Fraud Detection
  • Risk Analysis and Management

If we talk about data mining techniques for marketing analysis and management then it helps in customer profiling and to know that customer requirement identification. Cross-market analysis can also be performed through its result and target marketing can be done with the help of clusters that are created through data mining. Customer purchasing pattern can be identified and the summary can be provided.

Data Mining Applications

Credit card and telecommunication service frauds can be identified through data mining. If we talk about telecommunication fraud identification then duration, destination and day and time of the call can be identified. Patterns that deviate from expected norms can be identified.

The corporate risk that can be related to resource planning, competition and finance planning and asset evaluation can be analyzed and managed through data mining.

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Final Words

Finally, we can say that data mining is not about storing the data and information. It can help the organizations in ease the decision-making process by providing timely and managed information. Various data mining tools are used to execute the steps that are related to data mining. Data patterns can reveal much information about the data patterns. That’s all for the day. If you wanted to more explore Data mining then start the Data analytics course at JanBask Training right away.

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JanBask Training

JanBask Training is a leading Global Online Training Provider through Live Sessions. The Live classes provide a blended approach of hands on experience along with theoretical knowledge which is driven by certified professionals.



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