Data Science and Data Mining are two of the most important fields in technology. Both of these areas revolve around data.
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However, they use the data in two different ways. Furthermore, the knowledge required to work in both these fields is also different. The following article provides an overview of Data Mining.
What is Data Mining
?Data mining is the process of classifying and organizing large data sets to identify patterns and establish relationships to solve problems. subject by data analysis. Data mining MCUs enable businesses to predict future trends.
Data mining is a complex process that includes in-depth data warehousing as well as computational technologies. Furthermore, Data Mining is not only limited to data extraction, but is also used for transformation, cleaning, data integration, and pattern analysis.
There are various important parameters in Data Mining, such as association, classification, clustering, and forecasting rules. Some key features of Data Mining:
Predict patterns based on trends in data.Calculate predictive resultsGenerate feedback for analysisFocus on larger databases.Intuitive data clustering
Steps in Data Mining
Important steps when Data Mining include:
Step 1: Data Cleaning – In this step, the data is cleaned so that there is no noise or anomaly in the data.
Step 2: Data Integration – During data integration, multiple data sources will combine into one.
Step 3: Data Selection – In this step, data is extracted from the database.
Step 4: Data Transformation – In this step, the data will be transformed to perform summary analysis as well as aggregation operations.
Step 5: Data Mining – In this step, we extract useful data from the existing data pool.
Step 6: Sample Evaluation – We analyze some of the patterns present in the data.
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Step 7: Present the information – In the final step, the information will be presented in the form of trees, tables, graphs, and matrices.
Steps in Data Mining
Application of Data Mining
There are many common applications of Data Mining such as:
Market and stock analysis Fraud detection Risk management and business analyticsAnalysis of customer lifetime valueDiscover 10 more data mining apps
Data Mining Tools
Data Mining Tools
As one of the most popular tools for data mining, RapidMiner is written on the Java platform but requires no coding to operate. Moreover, it provides various data mining functions like data preprocessing, data representation, filtering, clustering, etc.
Weka is an open source data mining software developed at the University of Wichita. Like RapidMiner, Weka has no coding and uses a simple GUI.
Using Weka, you can call machine learning algorithms directly or import them in Java code. It provides a wide range of tools like visualization, preprocessing, classification, clustering, etc.
KNime is a powerful data mining suite, mainly used for data preprocessing, that is, ETL: Extract, Transform & Load. Furthermore, it integrates various components of machine science and data mining to provide an inclusive platform for all the right operations.
Apache Mahout is an extension of the Big Data Hadoop Platform. The developers at Apache developed Mahout to address the growing need for data mining and analytics in Hadoop.
As a result, it contains various machine learning functions like classification, regression, clustering, etc.
Oracle DataMining is a great tool for classifying, analyzing, and predicting data. It allows users to perform data mining on SQL database to extract frames and histograms.
For data, warehousing is a necessary requirement. TeraData, also known as TeraData Database provides a repository service of data mining tools.
It can cache data based on their usage, that is, it stores less frequently used data in the ‘slow’ section and allows quick access to frequently used data.
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Orange software is known for integrating data mining and machine learning tools. It is written in Python and provides an aesthetic and interactive visualization for the user.
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