Found 427 Articles for Data Mining

Data reconciliation is represented as a phase of verification of records during data migration. In this phase, target data is compared with source information to provide that the migration structure is assigning data. Data validation and reconciliation define a technology that facilitates numerical models to process data.An essential aspect in providing the quality of the information in business intelligence is the consistency of the information. Like a data warehouse, business intelligence combines and converts data and saves it so that it is made accessible for analysis and interpretation.The consistency of the data among the several process steps has to be ... Read More

The extraction method is immensely dependent on the source rule and also on the business requirement in the target data warehouse environment. The estimated bulk of the information to be extracted and the phase in the ETL procedure (original load or preservation of records) can also force the determination of how to extract, from a logical and a physical view. There are two types of extraction methods including Logical Extraction Methods and Physical Extraction Methods.Logical Extraction MethodsThere are two types of logical extraction are as follows −Full Extraction − The data is extracted entirely from the source system. Because this ... Read More

Extraction is the service of extracting information from a source system for additional help in a data warehouse environment. It is the first procedure of the ETL process. After the extraction, this data can be changed and loaded into the data warehouse. The source systems for a data warehouse are usually transaction processing software. It is the source systems for a sales analysis data warehouse can be an order entry system that data all of the current order activities.Data extraction is where data is considered and moved through to fetch relevant information from data sources (such as database) in a ... Read More

An artificial neural network is a system located on the services of biological neural networks. It is a simulation of a biological neural system. The characteristic of artificial neural networks is that there are multiple architectures, which consequently needed several methods of algorithms, but despite being a complex system, a neural network is nearly simple.These networks are among the unique signal-processing technologies in the director’s toolbox. The area is highly interdisciplinary, but this method will restrict the look to the engineering outlook.In engineering, neural networks deliver two important functions as pattern classifiers and as non-linear adaptive filters. An Artificial Neural ... Read More

Pruning is the procedure that decreases the size of decision trees. It can decrease the risk of overfitting by defining the size of the tree or eliminating areas of the tree that support little power. Pruning supports by trimming the branches that follow anomalies in the training information because of noise or outliers and supports the original tree in a method that enhances the generalization efficiency of the tree.Various methods generally use statistical measures to delete the least reliable departments, frequently resulting in quicker classification and an improvement in the capability of the tree to properly classify independent test data.There ... Read More

A decision tree is a flow-chart-like tree mechanism, where each internal node indicates a test on an attribute, each department defines an outcome of the test, and leaf nodes describe classes or class distributions. The highest node in a tree is the root node.Algorithms for learning Decision TreesAlgorithm − Create a decision tree from the given training information.Input − The training samples, samples, described by discrete-valued attributes; the set of students attributes, attribute-list.Output − A decision tree.MethodCreate a node N;If samples are all of the same class, C thenReturn N as a leaf node labeled with the class CIf the ... Read More

There are two types of statistical-based algorithms which are as follows −Regression − Regression issues deal with the evaluation of an output value located on input values. When utilized for classification, the input values are values from the database and the output values define the classes. Regression can be used to clarify classification issues, but it is used for different applications including forecasting. The elementary form of regression is simple linear regression that includes only one predictor and a prediction.Regression can be used to implement classification using two various methods which are as follows −Division − The data are divided ... Read More

There are the following pre-processing steps that can be used to the data to facilitate boost the accuracy, effectiveness, and scalability of the classification or prediction phase which are as follows −Data cleaning − This defines the pre-processing of data to eliminate or reduce noise by using smoothing methods and the operation of missing values (e.g., by restoring a missing value with the most generally appearing value for that attribute, or with the best probable value established on statistics). Although various classification algorithms have some structures for managing noisy or missing information, this step can support reducing confusion during learning.Relevance ... Read More

Classification is a data mining approach used to forecast team membership for data instances. It is a two-step procedure. In the first step, a model is built defining a predetermined set of data classes or approaches. The model is developed by considering database tuples defined by attributes.Each tuple is considered to belong to a predefined class, as decided by one of the attributes, known as the class label attribute. In the framework of classification, data tuples are also defined as samples, examples, or objects. The data tuples analyzed to develop the model jointly form the training data set. The single ... Read More

Genetic algorithms are mathematical structures using the procedure of genetic inheritance. They have been successfully used to a broad variety of analytic issues. Data mining can connect human understanding with automatic analysis of information to find a design or key relationships.Given a large database represented over several variables, the objective is to effectively find the most interesting design in the database. Genetic algorithms have been used to recognize interesting designs in some software. They generally are used in data mining to enhance the execution of other algorithms, such as decision tree algorithms, another association rule.Genetic algorithms needed a specific data ... Read More