Understanding Data Mining Architecture In Detail
Table of content:
- Database or Data Warehouse Server
- Data Mining Engine
- Pattern Evaluation Modules
- Graphical Interface User
- Types of Data Mining Architecture
- Data Mining Techniques
- Required Technological Drivers
In the world of information, it is as much of a challenge to extract the correct information as to discover something. Data mining is the technique of extracting knowledge from huge amounts of data stored in data sources such as file systems, data warehouses, and databases. This information is useful in various fields such as business strategies, scientific, medical research, governments, and individuals.
This huge amount of data is stored in various forms of documents like a data warehouse, database, files, spreadsheets, and the world wide web. The details of the components are as follows:
Database or Data Warehouse Server
The database server is where the data is collected once it is received from various data sources. This data becomes ready to be processed, and therefore the server manages the data retrieval.
Example — MarkLogic, Oracle, Amazon Redshift
Use Case — The original data is stored in the database or data warehouse server and is ready to be processed.
Data Mining Engine
In data mining, the engine is one of the main components and is the most vital part of the driving force that handles all the requests and manages them and is used to contain several modules. The data mining engine consists of various tools and software used to gain information and knowledge from data extracted from data sources and stored within several data warehouses.
Example — R language, Oracle Data Mining
Use Case — It consists of instruments and software that are used to extract knowledge and insights from data acquired from various data sources and stored in the data warehouse.
Pattern Evaluation Modules
This evaluation technique of the modules is mainly used to measure the interestingness of all those patterns used for calculating the basic level of the threshold value and is used to interact with the data mining engine to coordinate in the evaluation of other modules. In simple words, the main purpose of this component is to look out and search for all the interesting and useable patterns for producing better results based on the user requests.
Use case — This segment frequently uses stake measures in conjunction with data mining modules to narrow the search to interesting patterns. To filter out detected patterns, it could use a stake threshold. The pattern evaluation module, on the other hand, may be coordinated with the mining module, depending on how the data mining algorithms are implemented.
Graphical Interface User
The Graphical Interface User is used to establish a better sense of contact between the user and the data mining system, that is it helps the user to communicate effectively with the data mining system. Thereby helping users to access and use the system more efficiently and easily to keep them devoid of any problem arising in the process of abstraction by displaying the most relevant and accurate components to the users. This is achieved by the interaction of the module with the overall set of a data mining system to produce an accurate output that is easily shown to the user in a much more simplified way.
Example — Oracle Data Miner, IBM Data Mining GUI
Use Case — Allows the user to utilize the system quickly and effectively without having to understand the process’s intricacy. When the user specifies a query or a task, this module works with the data mining system to present the results.
Types of Data Mining Architecture
Let’s now look at the four types of Data Mining Architecture:
1. No-coupling Data Mining
A no-coupling data mining system retrieves data from a particular data source. It does not use any functionality of a database though it is quite an efficient and accurate way to retrieve data. It extracts data from a particular data source.
The no coupling architecture for data mining is not much efficient and can only be used for performing very simple data mining processes.
Working example — Collecting data from a source then process the data using mining algorithms. The result of the process is stored in another file.
2. Loose Coupling Data
In loose coupling Data architecture, the data retrieval is achieved from the database by the data mining systems and is stored in them. Memory-based data mining architecture is achieved by this mining.
Working example — The data mining system employs some database functions before retrieving data from the data respiratory. Finally, it saves the results to a file or a specific location in a database.
3. Semi Tight Data Mining
It tends to make use of a variety of data warehouse system’s advantages. Sorting, indexing, and aggregation are all part of it. For greater efficiency, an interim result can be saved in the database in this design.
Working example — A database is linked to a data mining system, and the database can also provide efficient implementations of a few data mining primitives.
4. Tight Coupling Data Mining
A data warehouse is one of the most significant components in this architecture, and its features are used to perform data mining operations. Scalability, performance, and integrated data are all features of this architecture. Tight-coupling data mining architecture is divided into three tiers:
- Data Layer: The data layer might be a database or data warehouse system. This layer serves as a connection point for all data sources. The data layer stores the results of data mining so that they can be displayed to the end user in the form of reports or other forms of visualization.
- Data Mining Application Layer: Data is retrieved from the database using the data mining application layer. To transform data into the required format, some transformation methods can be run here. After then, numerous data mining methods are used to process the data.
- Front End Layer: For end-user interaction with the data mining system, the front-end layer provides an easy and friendly user interface. The results of data mining are displayed in a graphic format.
Working example — The database or data warehouse system is seamlessly connected with the data mining system. The data mining subsystem is viewed as one of the information system’s functional components.
Data Mining Techniques
Association, classification, clustering, prediction, sequential patterns, and regression are some of the key data mining techniques that have been developed and employed. These are as follows:
1. Decision Trees: It’s paradigm is simple to understand, decision trees are one of the most widely used data mining techniques. The root of the decision tree in a decision tree approach is a basic query or condition with several solutions. Each response is followed by a series of questions or criteria that assist us determine the data so that we can make a final choice based on it.
Problems like predicting the loan eligibility process from given data can be solved using decision trees.
2. Sequential Patterns: Sequential patterns analysis is a data mining technique for discovering or identifying similar patterns, regular events, or trends in transaction data across time. Businesses can find a collection of things that customers buy together at different periods throughout the year using historical transaction data in sales. Then, based on their purchasing frequency in the past, firms can utilise this information to recommend that clients buy it at better deals.
To serialize a tree structure, sequential tree implementations can be employed. The process of storing an object as a series of bytes is known as serialization.
3. Clustering: Clustering is a data mining technique that uses an automatic technique to create a meaningful or useful cluster of items with similar features. Clustering approaches define classifications and place objects within them, whereas classification techniques assign things to predefined classes.
Clustering is a technique used by retailers to find groups of homes that are similar to one another.
4. Prediction: Prediction is a data mining technique that discovers the association between independent factors as well as the relationship between dependent and independent variables, as its name implies. For example, if we take the sale to be an independent variable and profit to be a dependent variable, the prediction analysis technique can be used to anticipate future profit in the sale. We can then build a fitted regression curve that is utilized for profit projection based on historical sale and profit data.
Prediction employs algorithm-based tools to search a customer database for historical transactions in order to validate theories regarding future transaction volumes.
5. Association: One of the most well-known data mining approaches is association. A pattern is discovered in association based on a relationship between goods in the same transaction. The association technique is also known as the relation technique for this reason. In market basket analysis, the association technique is used to find a group of products that buyers regularly purchase together.
For example, in a multi-item transaction, Association Rule Mining aims to discover the rules that control how or why such products/items are frequently purchased together.
6. Classification: Classification is a well-known machine-learning-based data mining technique. In essence, classification is the process of categorizing each item in a piece of data into one of a number of specified classes or groupings. Mathematical approaches such as decision trees, linear programming, neural networks, and statistics are used in the classification procedure. We produce classification software that can learn how to classify data items into categories.
For example, classification is defined as the grouping of patients based on their known medical data and treatment outcomes.
Required Technological Drivers
Data mining applications are available for machines of all sizes. Mainframes, workstations, clouds, clients, and servers are just a few examples. Enterprise apps range in size from 10 GB to 100 TB. NCR systems are preferred for delivering applications that are larger than 100 terabytes. The technological drivers required are as follows:
1. DatabaseSize: We need powerful systems to maintain and process massive amounts of data. Estimating the size of a database can also help you figure out if the design has to be tweaked. For example, you may find that the database’s expected size is too huge for your business to implement, and that more normalization is required. On the other hand, the estimated size could be smaller than anticipated. You’d be able to de-normalize the database and enhance query performance as a result.
2. Query Complexity: We need a more powerful system to analyze the complicated and big number of requests. Data mining queries are beneficial for a variety of reasons. You can enter input values as parameters or in a batch to generate a statistical overview of the training data.
It can also be used to extract patterns and rules, as well as regression formulae and other patterns-related calculations. It’s possible to get information on individual cases included in the model, like data that wasn’t used in the study.
We conclude that data mining is a necessary and powerful tool for us to study data patterns, understand and predict outcomes. Hence, different processes, architectures and specific techniques equip us with fairly powerful toolset to solve any kind of problem.
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