Application of the Data Mining Techniques and Tools Assignment Sample
Data mining is the process of extracting and discovering large sets of data patterns that involve statistics and database systems.
Data mining involves a process of data analysis for the transformation of information in data into a formal structure and using it for other purposes. It has the ultimate goal of extracting large amounts of information from the data and not the data itself.
The data mining process includes using databases, data processing, visualization, data management process, online updating and consideration of complexity, etc.
It uses machine learning and statistical data models for a large amount of extraction of data patterns.
In this assignment sample, we shall discuss broadly the application of various tools and techniques in data mining.
Application of the Various Data Mining Techniques
There are many techniques used for the data mining process which use the raw data into actionable insights. These techniques of data mining shall provide long-term benefits to the business venture.
The many data mining techniques usually range from cleaning and preparation of the data, data storing, or warehousing to turning them into artificial intelligence.
Data Cleansing and Data Preparation
The data mining process involves the most vital part called data cleansing and data preparation. It is an initial technique of cleansing the raw data and turning it into a structured format with the help of many different analytic methods. It includes data migration, data integration, data modeling, ETL, ELT, and aggregation, etc. This step is valuable for checking the quality of data and adding value to the business venture.
Data Tracking Patterns
Data tracking patterns involve the identification and monitoring of data trends or patterns to make decisions based upon them about the business.
For example – Keeping a sales track and market patterns record to know what products and services are in demand and use this knowledge for the benefit of the business.
Classification of Data
Organizations can easily identify the different types of data into various categories based on their characteristics. This process of classification of the data requires a critical understanding and data analysis of data of different nature.
Outlier Detection of Data
An outlier detection technique can help in the identification of the anomalies of data so that the business can be prepared for any future events or occurrences and to achieve business objectives.
The mechanism of clustering involves using graphics to show and demonstrate the distribution of data about the different metrics with the help of different types of colors. The clustering of data involves the usage of visual or graphic representation of data with colors.
A prediction is a very powerful tool or technique for data mining that can use patterns that are found in the current or historical data providing insight into the future. The advanced tool of prediction and aspects includes machine learning and artificial intelligence.
The decision trees are the specific model of prediction that allows the organization to mine the data. It is known as the white-box machine learning technique. This technique of decision trees helps the users to get a clear understanding of the data inputs affecting outputs.
The combination of decision tree models is called a random forest which is known as black-box machine learning. It makes the user learn and understand the outputs based on their inputs.
Data visualization is dynamic by nature where the different colors are being used for different trends and data patterns. Dashboards are a popular tool used by an organization that uses different metrics to visually highlight the patterns in data using numerical outputs.
Data storing and warehousing is one of the most integral and traditional techniques which does involve the storing of structured data about the database management systems.
Such data storing helps in the analysis for business intelligence, reporting, and basic dashboard capabilities. There are cloud data warehouses used these days.
Machine Learning and an Artificial Intelligence
Some of the well-known advanced developments in data mining include machine learning and artificial intelligence. This ML and AI help with deep learning, text analytics, speech recognition, and computer vision, etc. It is used for determining the value of semi and unstructured data.
Application of Some Data Mining Tools
There are many numerous data mining tools which are available in the market. Choosing any of the data mining tools for an application depends upon the number of factors that need to be taken into consideration for the business before making any type of investment.
Rapid Miner is the data science software platform that can easily integrate the environment for data preparation, deep learning, machine learning, text mining, and predictive analysis. This program is in the Java programming language.
Oracle Data Mining
The Oracle Advanced Analytics Database has a system of work with a powerful data algorithm to target the customers. It identifies the cross-selling opportunities and anomalies.
A python is a tool used by the users to identify it as an open-source language that can start building the datasets by doing the complex affinity analysis. It uses various visualization programs and basic programming.
Orange is an open-source data visualization with the data mining techniques of machine learning. It has visual programming for exploratory data analysis and data interaction.
Such visual programming platform in Orange is performed through an interface where workflows are created by linking user-designed widgets.
Kaggle is the world’s largest community of data scientists and machine learners. It is being extended to a public cloud-based data science platform that helps in solving the problems, recruiting a strong team, and accentuating the power of data science.
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