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Data mining is the process in which information that was previously unknown, which could be potentially very useful, is extracted from a very vast dataset. Data mining architecture or architecture of data mining techniques is nothing but the various components which constitute the entire process of data mining. Learn data science to gain expertise in data mining and remain competitive in the market.
Data Mining Architecture Components
Let’s take a look at the components which make the entire data mining architecture.
1. Sources of Data
The place where we get our data to work upon is known as the data source or the source of the data. There are many documentations presented, and one might also argue that the whole World Wide Web (WWW) is a big data warehouse. The data can be anywhere, and some might reside in text files, a standard spreadsheet document, or any other viable source like the internet.
2. Database or Data Warehouse Server
The server is the place that holds all the data which is ready to be processed. The fetching of data works upon the user’s request, and, thus, the actual datasets can be very personal.
3. Data Mining Engine
The field of data mining is incomplete without what is arguably the most crucial component of it, known as a data mining engine. It usually contains a lot of modules that can be used to perform a variety of tasks. The tasks which can be performed can be association, characterization, prediction, clustering, classification, etc.
4. Modules for Pattern Evaluation
This module of the architecture is mainly employed to measure how interesting the pattern that has been devised is actually. For the evaluation purpose, usually, a threshold value is used. Another critical thing to note here is that this module has a direct link of interaction with the data mining engine, whose main aim is to find interesting patterns.
5. GUI or Graphical User Interface
As the name suggests, this module of the architecture is what interacts with the user. GUI serves as the much-needed link between the user and the system of data mining. GUI’s main job is to hide the complexities involving the entire process of data mining and provide the user with an easy to use and understand module which would allow them to get an answer to their queries in an easy to understand fashion.
6. Knowledge Base
The base of all the knowledge is vital for any data mining architecture. The knowledge base is usually used as the guiding beacon for the pattern of the results. It might also contain the data from what the users have experienced. The data mining engine interacts with the knowledge base often to both increase the reliability and accuracy of the final result. Even the pattern evaluation module has a link to the knowledge base. It interacts with the knowledge base on a regular interval to get various inputs and updates from it.
Types of data mining architecture
There are four different types of architecture which have been listed below:
1. No-coupling Data Mining
No-coupling architecture typically does not make the use of any functionality of the database. What no-coupling usually does is that it retrieves the required data from one or one particular source of data. That’s it; this type of architecture does not take any advantages whatsoever of the database in question. Because of this specific issue, no-coupling is usually considered a poor choice of architecture for the system of data mining. Still, it is often used for elementary processes involving data mining.
2. Loose coupling Data Mining
Loose coupling data mining process employs a database to do the bidding of retrieval of the data. After it is done finding and bringing the data, it stores the data into these databases. This type of architecture is often used for memory-based data mining systems that do not require high scalability and high performance.
3. Semi-Tight coupling Data Mining
Semi-Tight architecture makes uses of various features of the warehouse of data. These features of data warehouse systems are usually used to perform some tasks pertaining to data mining. Tasks like indexing, sorting, and aggregation are the ones that are generally performed.
4. Tight-coupling Data Mining
The tight-coupling architecture differs from the rest in its treatment of data warehouses. Tight-coupling treats the data warehouse as a component to retrieve the information. It also makes use of all the features that you would find in the databases or the data warehouses to perform various data mining tasks. This type of architecture is usually known for its scalability, integrated information, and high performance. There are three tiers of this architecture which are listed below:
5. Data layer
Data layer can be defined as the database or the system of data warehouses. The results of data mining are usually stored in this data layer. The data that this data layer houses can then be further used to present the data to the end-user in different forms like reports or some other kind of visualization.
6. Data Mining Application layer
The job of Data mining application layer is to find and fetch the data from a given database. Usually, some data transformation has to be performed here to get the data into the format, which has been desired by the end-user.
7. Front end layer
This layer has virtually the same job as a GUI. The front-end layer provides intuitive and friendly interaction with the user. The result of the data mining is usually visualized as some form or the other to the user by making use of this front-end layer.
Techniques of Data Mining
There are several data mining techniques which are available for the user to make use of; some of them are listed below:
1. Decision Trees
Decision trees are the most common technique for the mining of the data because of the complexity or lack thereof in this particular algorithm. The root of the tree is a condition. Each answer then builds upon this condition by leading us in a specific way, which will eventually help us to reach the final decision.
2. Sequential Patterns
Sequential patterns are usually used to discover events that occur regularly or trends that can be found in any transactional data.
Clustering is a technique that automatically defines different classes based on the form of the object. The classes thus formed will then be used to place other similar kinds of objects in them.
This technique is usually employed when we are required to accurately determine an outcome that is yet to occur. These predictions are made by accurately establishing the relationship between independent and dependent entities.
This technique is based out of a similar machine learning algorithm with the same name. This technique of classification is used to classify each item in question into predefined groups by making use of mathematical techniques such as linear programming, decision trees, neural networks, etc.
Due to the leaps and bounds made in the field of technology, the power and prowess of processing have significantly increased. This increment in technology has enabled us to go further and beyond the traditionally tedious and time-consuming ways of data processing, allowing us to get more complex datasets to gain insights that were earlier deemed impossible. This gave birth to the field of data mining. Data mining is a new upcoming field that has the potential to change the world as we know it.
Data mining architecture or architecture of data mining system is how data mining is done. Thus, having knowledge of architecture is equally, if not more, important to having knowledge about the field itself.
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What is the future scope of data mining?
Data Mining is an immensely useful procedure for extracting previously unknown information from a huge chunk of data. Extracting actionable information is necessary for the growth and benefit of every business or organization. Data mining is the process that makes the decision-making process easier for organizations based on the available data.
This is why there is a huge demand for data tmining analysts but there are not enough qualified professionals to take up the job. With data being the most important factor driving business decisions, there is a huge scope for data mining professionals. So, if you are thinking about building a career in the field of data mining, then you are definitely looking towards a bright future.
What are the top 5 data mining methods?
In today's world, we are all surrounded by data from every side. This situation is going to become more intense with time. Knowledge is deeply buried inside this data, and it is necessary to implement certain strategies that can clear out the noise and provide actionable information from the chunk of data. Without actionable information, data is said to be useless and ineffective.
The top 5 data mining methods for creating optimal results for all the datasets are Classification analysis, Association rule learning, Clustering analysis, Regression analysis, and Anomaly or outlier detection.
What are the different applications of data mining?
Data is present everywhere, and this is why data mining is being widely used in different sectors. With everything moving towards digitization, organizations' amount of data being collected and stored is exponentially increasing. Data mining systems are generated in every sector, while there are still plenty of challenges these systems face.
The trend of data mining is at an entirely new level, and its applications are seen in almost every industry. Some of the key industries where the applications of data mining are widely seen are financial data analysis, retail industry, telecommunication industry, biological data analysis, and intrusion detection.