Overview of Data Mining Architecture
The data mining is the way of finding and exploring the patterns basic or of advanced level in a complicated set of large data sets which involves the methods placed at the intersection of statistics, machine learning and also database systems. It can be said to be an interdisciplinary field of statistics and computer sciences where the goal is to extract the information using intelligent methods and techniques from a particular set of data by means of extraction and thereby transforming the data. The data management activities and data preprocessing activities along with inference considerations are also taken into consideration. In this article, we will dive deep into the architecture of data mining.
Data Mining Architecture
The data mining is the technique of extracting interesting knowledge from a set of huge amounts of data which then is stored in many data sources such as file systems, data warehouses, databases. The primary components of the data mining architecture involve –
1. Data Sources
A huge variety of present documents such as data warehouse, database, www or popularly called a World wide web which becomes the actual data sources. Most of the times, it can also be the case that the data is not present in any of these golden sources but only in the form of text files, plain files or sequence files or spreadsheets and then the data needs to be processed in a very similar way as the processing would be done upon the data received from golden sources. Most of the major chunk of data today is received from the internet or the world wide web as everything which is present on the internet today is data in some form or another which forms some form of information repository units.
Before the data is processed ahead the different processes through which it goes involves data cleansing, integration, and selection before finally the data is passed onto the database or any of the EDW (enterprise data warehouse ) server. The major challenge which lies at times with this set of data is different levels of sources and a wide array of data formats which forms the data components. Therefore the data cannot be directly used for processing in its naïve state but processed, transformed and crafted in a much more usable way. This way, the reliability and completeness of the data are also ensured. So, the primary step involves data collection, cleaning and integration, and post that only the relevant data is passed forward. All this activity forms a part of a separate set of tools and techniques.
2. Data Warehouse Server or Database
The database server is the actual space where the data is contained once it is received from various number of data sources. The server contains the actual set of data which becomes ready to be processed and therefore the server manages the data retrieval. All this activity is based on the request for data mining of the person.
3. Data Mining Engine
In the case of data mining, the engine forms the core component and is the most vital part, or to say the driving force which handles all the requests and manages them and is used to contain a number of modules. The number of modules present includes mining tasks such as classification technique, association technique, regression technique, characterization, prediction and clustering, time series analysis, naive Bayes, support vector machines, ensemble methods, boosting and bagging techniques, random forests, decision trees, etc.
4. Pattern Evaluation Modules
This evaluation technique of the modules is mainly responsible for measuring the interestingness of all those patterns which are being used for calculating the basic level of the threshold value and also is used to interact with the data mining engine to coordinate in the evaluation of other modules. All in all, the main purpose of this component is to look out and search for all the interesting and useable patterns which could make the data of comparatively better quality.
5. Graphical User Interface
When the data is communicated with the engines and among various pattern evaluation of modules, it becomes a necessity to interact with the various components present and make it more user friendly so that the efficient and effective use of all the present components could be made and therefore arises the need of a graphical user interface popularly known as GUI.
This is used to establish a sense of contact between the user and the data mining system thereby helping users to access and use the system efficiently and easily to keep them devoid of any complexity which has been arising in the process. This is a form of abstraction where only the relevant components are displayed to the users and all the complexities and functionalities responsible to build the system are hidden for the sake of simplicity. Whenever the user submits a query, the module then interacts with the overall set of a data mining system to produce a relevant output which could be easily shown to the user in a much more understandable manner.
6. Knowledge Base
This is the component that forms the base of the overall data mining process as it helps in guiding the search or in the evaluation of interestingness of the patterns formed. This knowledgebase consists of user beliefs and also the data obtained from user experiences which are in turn helpful in the data mining process. The engine might get its set of inputs from the created knowledge base and thereby provides more efficient, accurate and reliable results.
Data mining is one of the most important techniques today which deals with data management and data processing which forms the backbone of any organization. Analysis of data in any organization will bring fruitful results. Each and every component of the data mining technique and architecture has its own way of performing responsibilities and also in completing data mining efficiently. The different modules are needed to interact correctly so as to produce a valuable result and complete the complex procedure of data mining successfully by providing the right set of information to the business.
This has been a guide to Data Mining Architecture. Here we discuss the brief overview with primary components of the data mining Architecture. You can also go through our other suggested articles to learn more –