As the amount of data collected grows, most businesses and organisations are turning to Data Mining for analysis. Data Mining helps identify patterns and discover trends that further help in making right decisions for the company and its growth. The Data Mining method is applied to the acquired data in order to derive module predictions and find interesting trends. Here, most of the data is stored on a single site.
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But, there are many applications where the data is inherently distributed.
With the developments in Data Mining, the concept of Distributed Data Dining (DDM) came into action. Distributed Data Mining involves the mining of datasets regardless of their physical locations. Its main role is to extract information from the distributed heterogeneous Databases and use it for decision-making.
Here, we will discuss Distributed Data Mining, its architecture, processes, algorithms, and benefits in detail.
Data Mining is a process of sorting large datasets to discover valuable information and use it for analysis to increase efficiency in business operations. It uses software, algorithms, and other statistical methods to identify patterns and relationships that further help in resolving business issues. Data mining processes are mostly used in marketing, risk management, cybersecurity, mathematics, medical diagnosis, and other fields.
To uncover trends and handle business difficulties, most companies employ Data Mining to extract hidden patterns and information from huge datasets.
It includes machine learning and statistical analysis in addition to data management activities. Organizations may use Data Mining to discover possible customer service issues, enhance lead conversion rates, recognise market trends and estimate product demand, better analyse cybersecurity and other risks, and decrease redundancy, among other things.
Distributed Data Mining process involves the mining of distributed datasets stored in multiple local Databases. Often the data is distributed among several Databases, which makes it more prone to security risks. With the help of Distributed Data Mining, admins can perform data analysis and mining operations in a distributed manner to discover knowledge and use it efficiently for business operations.
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Firstly, let us discuss the architecture of a Data Mining system. In our shared image, the left section shows that the Data Mining has a traditional Data Warehouse-based architecture. Here, for centralized Data Mining, the admin needs to upload critical data to the Data Warehouse.
However, this architecture is not supported by Distributed Data Mining because it lacks proper use of distributed resources, supports long response time, and comprises characteristics of a centralized Data Mining algorithm.
The only solution to this is to set up a distributed application for processing controlled by potential resources and human factors. If you look at the right image, DDM performs all the Data Mining operations on the basis of available resources and types of operations. It picks the site to access data based on storage, computing, and connection capabilities and then conducts all processes centrally.
Before the mining process begins, the data is prepared by selecting the appropriate information, eliminating noisy data, and integrating data from multiple Databases. Data Cleansing, Integration, Reduction, Transformation, Mining, Pattern Evaluation, and Knowledge Representation are all components of the Data Mining process. Have a look at the processes involved in DDM in detail:
Data Cleaning is the primary step under which all the noisy, inconsistent, or incomplete data is removed from the collection. Simply, it removes any noisy data that isn’t required for the analysis.
As part of the Data Integration process, all information that comes from different datasets, including databases, data cubes, data warehouses, or files are combined to perform analysis. This step aids in improving the efficiency and speed of the data mining process.
This technique helps in sorting and obtaining only relevant data from the collection for analysis. It focuses on reducing the number of attributes and original data volume while maintaining integrity.
For the Data Mining process, the data is aggregated and converted here. As a result, understanding and identifying trends in the mining process is simplified.
Under the Data Mining process, experts look for new patterns and try gathering information from large datasets to perform analysis and resolve business issues.
Based on the interestingness measures, some interesting patterns are identified. After identifying patterns, Data Summarization and Visualization techniques are practised to evaluate and make it easier for the user to understand the data.
Here all the mined information and data is visualized in the form of reports and presented using knowledge representation tools to the user.
Distributed Data Mining Algorithms can be classified as:
Initially, Data Mining was limited to sorting centralized datasets stored at a single site. But with more and more usage of data, multiple interrelated Databases were created & distributed over a large computer network.
The Data Mining technique is incapable of dealing with distributed datasets. As a result, the concept of distributed data mining was introduced. Here are a few benefits of distributed data mining:
Every day a large amount of data is generated over different sites, and consequently, there is a need to analyze the generated information for better decision making and growth. To perform analysis on these large datasets, many businesses use techniques like Data Mining.
Data Mining is a process where data is collected from different sources, sorted, and turned into useful information. It involves the use of software, algorithms, and other statistical methods to discover patterns and trends in large datasets. The generated information further helps businesses learn more about their customers and how to boost sales, reduce costs, etc. There are various advantages of Data Mining and Distributed Data Mining.
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Share your experience of learning about Distributed Data Mining! Let us know in the comments below!
Distribution of data in data mining refers to the way data points are spread or dispersed across different values, ranges, or categories within a dataset.
A distributed algorithm in data mining is a method that processes data across multiple computing nodes or machines to handle large-scale datasets efficiently.
a) Descriptive Data Mining
b) Predictive Data Mining
c) Prescriptive Data Mining
Hitesh is a skilled freelance writer in the data industry, known for his engaging content on data analytics, machine learning, AI, big data, and business intelligence. With a robust Linux and Cloud Computing background, he combines analytical thinking and problem-solving prowess to deliver cutting-edge insights. Hitesh leverages his Docker, Kubernetes, AWS, and Azure expertise to architect scalable data solutions that drive business growth.