Data mining is a very important process where potentially useful and previously unknown information is extracted from large volumes of data. There are a number of components involved in the data mining process.
These components constitute the architecture of a data mining system. The major components of any data mining system are data source, data warehouse server, data mining engine, pattern evaluation module, graphical user interface and knowledge base. You need large volumes of historical data for data mining to be successful.
Organizations usually store data in databases or data warehouses. Data warehouses may contain one or more databases, text files, spreadsheets or other kinds of information repositories. Sometimes, data may reside even in plain text files or spreadsheets. World Wide Web or the Internet is another big source of data.Data Mining & Business Intelligence - Tutorial #2 - Architecture Of Data Mining System
The data needs to be cleaned, integrated and selected before passing it to the database or data warehouse server. As the data is from different sources and in different formats, it cannot be used directly for the data mining process because the data might not be complete and reliable.
So, first data needs to be cleaned and integrated. Again, more data than required will be collected from different data sources and only the data of interest needs to be selected and passed to the server.
These processes are not as simple as we think. A number of techniques may be performed on the data as part of cleaning, integration and selection.
The database or data warehouse server contains the actual data that is ready to be processed. Hence, the server is responsible for retrieving the relevant data based on the data mining request of the user. The data mining engine is the core component of any data mining system. It consists of a number of modules for performing data mining tasks including association, classification, characterization, clustering, prediction, time-series analysis etc.
The pattern evaluation module is mainly responsible for the measure of interestingness of the pattern by using a threshold value. It interacts with the data mining engine to focus the search towards interesting patterns.
The graphical user interface module communicates between the user and the data mining system. This module helps the user use the system easily and efficiently without knowing the real complexity behind the process. When the user specifies a query or a task, this module interacts with the data mining system and displays the result in an easily understandable manner. The knowledge base is helpful in the whole data mining process. It might be useful for guiding the search or evaluating the interestingness of the result patterns.
The knowledge base might even contain user beliefs and data from user experiences that can be useful in the process of data mining.
The data mining engine might get inputs from the knowledge base to make the result more accurate and reliable. The pattern evaluation module interacts with the knowledge base on a regular basis to get inputs and also to update it. Each and every component of data mining system has its own role and importance in completing data mining efficiently.You access sources of data in a SQL Server database or any other data source to use for training, testing, or prediction.
The process of creating these solution objects has already been described elsewhere. For more information, see Data Mining Solutions. The following sections describe the logical architecture of the objects in a data mining solution. The data that you use in data mining is not stored in the data mining solution; only the bindings are stored. When you train the structure or model by processing, a statistical summary of the data is created and stored in a cache that can be persisted for use in later operations, or deleted after processing.
You combine disparate data within the Analysis Services data source view DSV object, which provides an abstraction layer on top of your data source. You can specify joins between tables, or add tables that have a many-to-one relationship to create nested table columns.
For more information about working with these objects programmatically, see Logical Architecture Overview Analysis Services - Multidimensional Data. A data mining structure is a logical data container that defines the data domain from which mining models are built. A single mining structure can support multiple mining models. When you need to use the data in the data mining solution, Analysis Services reads the data from the source and generates a cache of aggregates and other information.
By default this cache is persisted so that training data can be reused to support additional models. If you need to delete the cache, change the CacheMode property on the mining structure object to the value, ClearAfterProcessing. Analysis Services also provides the ability to separate your data into training and testing data sets, so that you can test your mining models on a representative, randomly selected set of data.
The data is not actually stored separately; rather, case data in the structure cache is marked with a property that indicates whether that particular case is used for training or for testing.
If the cache is deleted, that information cannot be retrieved. A data mining structure can contain nested tables.
A nested table provides additional detail about the case that is modeled in the primary data table. Before processing, a data mining model is only a combination of metadata properties. These properties specify a mining structure, specify a data mining algorithm, and a define collection of parameter and filter settings that affect how the data is processed.
When you process the model, the training data that was stored in the mining structure cache is used to generate patterns, based both on statistical properties of the data and on heuristics defined by the algorithm and its parameters. This is known as training the model.Data mining is the process of looking at large banks of information to generate new information.
Relying on techniques and technologies from the intersection of database management, statistics, and machine learning, specialists in data mining have dedicated their careers to better understanding how to process and draw conclusions from vast amounts of information.
But what are the techniques they use to make this happen? Tracking patterns. One of the most basic techniques in data mining is learning to recognize patterns in your data sets. This is usually a recognition of some aberration in your data happening at regular intervals, or an ebb and flow of a certain variable over time. For example, you might see that your sales of a certain product seem to spike just before the holidays, or notice that warmer weather drives more people to your website.
Classification is a more complex data mining technique that forces you to collect various attributes together into discernable categories, which you can then use to draw further conclusions, or serve some function.
You could then use these classifications to learn even more about those customers. Association is related to tracking patterns, but is more specific to dependently linked variables.
Outlier detection. You also need to be able to identify anomalies, or outliers in your data. Clustering is very similar to classification, but involves grouping chunks of data together based on their similarities. For example, you might choose to cluster different demographics of your audience into different packets based on how much disposable income they have, or how often they tend to shop at your store. Regression, used primarily as a form of planning and modeling, is used to identify the likelihood of a certain variable, given the presence of other variables.
For example, you could use it to project a certain price, based on other factors like availability, consumer demand, and competition. In many cases, just recognizing and understanding historical trends is enough to chart a somewhat accurate prediction of what will happen in the future. So do you need the latest and greatest machine learning technology to be able to apply these techniques? Not necessarily. As long as you apply the correct logic, and ask the right questions, you can walk away with conclusions that have the potential to revolutionize your enterprise.
Views: Tags: DataMiningTechniquesdataminingtechniques.There are four different types of layers which will always be present in Data Warehouse Architecture. The Data received by the Source Layer is feed into the Staging Layer where the first process that takes place with the acquired data is extraction. An important point about Data Warehouse is its efficiency.
To create an efficient Data Warehouse, we construct a framework known as the Business Analysis Framework. There are four types of views in regards to the design of a Data warehouse. This has been a guide to Data Warehouse Architecture. Your email address will not be published. Forgot Password? Popular Course in this category.
Email ID is incorrect. Provides a definite and consistent view of information as information from the data warehouse is used to create Data Marts. Reports can be generated easily as Data marts are created first and it is relatively easy to interact with data marts. Not as strong but data warehouse can be extended and the number of data marts can be created.Data mining is described as a process of discovering or extracting interesting knowledge from large amounts of data stored in multiple data sources such as file systems, databases, data warehouses …etc.
This knowledge contributes a lot of benefits to business strategies, scientific, medical research, governments, and individual. Business data is collected explosively every minute through business transactions and stored in relational database systems. In order to provide insight into the business processes, data warehouse systems have been built to provide analytical reports that help business users to make decisions. This question leads to four possible architectures of a data mining system as follows:.
And then we looked into a tight couple data mining architecture — the most desired, high performance and scalable data mining architecture. ZenTut Programming Made Easy. Data Mining Architecture. Introduction to Data mining Architecture Data mining is described as a process of discovering or extracting interesting knowledge from large amounts of data stored in multiple data sources such as file systems, databases, data warehouses …etc.
This question leads to four possible architectures of a data mining system as follows: No-coupling : in this architecture, data mining system does not utilize any functionality of a database or data warehouse system. A no-coupling data mining system retrieves data from a particular data source such as file system, processes data using major data mining algorithms and stores results into the file system.
The no-coupling data mining architecture does not take any advantages of database or data warehouse that is already very efficient in organizing, storing, accessing and retrieving data. The no-coupling architecture is considered a poor architecture for data mining system, however, it is used for simple data mining processes. Loose Coupling : in this architecture, data mining system uses the database or data warehouse for data retrieval. In loose coupling data mining architecture, data mining system retrieves data from the database or data warehouse, processes data using data mining algorithms and stores the result in those systems.
This architecture is mainly for memory-based data mining system that does not require high scalability and high performance. Semi-tight Coupling : in semi-tight coupling data mining architecture, besides linking to database or data warehouse system, data mining system uses several features of database or data warehouse systems to perform some data mining tasks including sorting, indexing, aggregation…etc.
In this architecture, some intermediate result can be stored in database or data warehouse system for better performance. Tight Coupling : in tight coupling data mining architecture, database or data warehouse is treated as an information retrieval component of data mining system using integration.
All the features of database or data warehouse are used to perform data mining tasks. This architecture provides system scalability, high performance, and integrated information. Tight-coupling data mining architecture Data Mining Architecture. Was this tutorial helpful? Yes No. Previous Tutorial: What is Data Mining.
Next Tutorial: Data Mining Applications. Return to top of page. All Rights Reserved.One can see that the term itself is a little bit confusing. In case of coal or diamond mining, the result of extraction process is coal or diamond.
But in case of Data Mining, the result of extraction process is not data!! Instead, the result of data mining is the patterns and knowledge that we gain at the end of the extraction process. Currently, Data Mining and Knowledge Discovery are used interchangeably. Now a days, data mining is used in almost all the places where a large amount of data is stored and processed.
Data and architecture design
Since banks have the transaction details and detailed profiles of their customers, they analyze all this data and try to find out patterns which help them predict that certain customers could be interested in personal loans etc. Main Purpose of Data Mining Basically, the information gathered from Data Mining helps to predict hidden patterns, future trends and behaviors and allowing businesses to take decisions.
Data Mining can be applied to any type of data e. Data Mining as a whole process The whole process of Data Mining comprises of three main phases: 1. Data Pre-processing — Data cleaning, integration, selection and transformation takes place 2. Data Extraction — Occurrence of exact data mining 3. Data Evaluation and Presentation — Analyzing and presenting results. Applications of Data Mining 1. Financial Analysis 2. Biological Analysis 3. Scientific Analysis 4.
Intrusion Detection 5. Fraud Detection 6. Research Analysis. Real life example of Data Mining — Market Basket Analysis Market Basket Analysis is a technique which gives the careful study of purchases done by a customer in a super market. The concept is basically applied to identify the items that are bought together by a customer. This analysis helps in promoting offers and deals by the companies. The same is done with the help of data mining.
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Writing code in comment? Please use ide. Load Comments.Data miningalso called knowledge discovery in databasesin computer sciencethe process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines tools from statistics and artificial intelligence such as neural networks and machine learning with database management to analyze large digital collections, known as data sets.
Data mining is widely used in business insurance, banking, retailscience research astronomy, medicineand government security detection of criminals and terrorists. The proliferation of numerous large, and sometimes connected, government and private databases has led to regulations to ensure that individual records are accurate and secure from unauthorized viewing or tampering.
Most types of data mining are targeted toward ascertaining general knowledge about a group rather than knowledge about specific individuals—a supermarket is less concerned about selling one more item to one person than about selling many items to many people—though pattern analysis also may be used to discern anomalous individual behaviour such as fraud or other criminal activity.
Data Mining Tutorial
As computer storage capacities increased during the s, many companies began to store more transactional data. The resulting record collections, often called data warehouses, were too large to be analyzed with traditional statistical approaches. Several computer science conferences and workshops were held to consider how recent advances in the field of artificial intelligence AI —such as discoveries from expert systemsgenetic algorithmsmachine learningand neural networks—could be adapted for knowledge discovery the preferred term in the computer science community.
This was also the period when many early data-mining companies were formed and products were introduced. One of the earliest successful applications of data mining, perhaps second only to marketing research, was credit-card - fraud detection. However, the wide variety of normal behaviours makes this challenging; no single distinction between normal and fraudulent behaviour works for everyone or all the time.
Every individual is likely to make some purchases that differ from the types he has made before, so relying on what is normal for a single individual is likely to give too many false alarms.
One approach to improving reliability is first to group individuals that have similar purchasing patterns, since group models are less sensitive to minor anomalies.
Data Warehouse Architecture
The complete data-mining process involves multiple steps, from understanding the goals of a project and what data are available to implementing process changes based on the final analysis. The three key computational steps are the model-learning process, model evaluation, and use of the model.
This division is clearest with classification of data. Model learning occurs when one algorithm is applied to data about which the group or class attribute is known in order to produce a classifier, or an algorithm learned from the data.
The classifier is then tested with an independent evaluation set that contains data with known attributes. If the model is sufficiently accurate, it can be used to classify data for which the target attribute is unknown. There are many types of data mining, typically divided by the kind of information attributes known and the type of knowledge sought from the data-mining model. Predictive modeling is used when the goal is to estimate the value of a particular target attribute and there exist sample training data for which values of that attribute are known.
An example is classification, which takes a set of data already divided into predefined groups and searches for patterns in the data that differentiate those groups. These discovered patterns then can be used to classify other data where the right group designation for the target attribute is unknown though other attributes may be known.