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

A neural network is an array of algorithms that endeavors to identify fundamental relationships in a set of data through a process that mimics the techniques the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial.Neural networks are applicable in virtually every situation in which a relationship between the predictor variables (independents, inputs) and predicted variables (dependents, outputs) exists, even when that relationship is very complex and not easy to articulate in the usual terms of “correlations” or “differences between groups.”There are various applications of Neural Networks which are as follows −Detection ... Read More

A neural network is a series of algorithms that endeavors to recognize basic relationships in a set of record through a process that mimics the way the human brain operates. In this method, neural networks defines systems of neurons, either organic or artificial.Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations after implementing a process of so-called learning from existing information. Neural Networks are one of the Data Mining techniques.The first phase is to ... Read More

Similarity measures provide the framework on which some data mining decisions are based. Tasks including classification and clustering generally consider the existence of some similarity measure, while fields with poor techniques to evaluate similarity often find that searching information is a cumbersome function.There are several applications of similarity measures are as follows −Information Retrieval − The goal of information retrieval (IR) systems is to meet user’s needs. In another terms, a need is generally manifested in the form of a short textual query introduced in the text box of some search engine online. IR systems generally do not directly answer ... Read More

Advantages of Data MiningThe advantage of data mining are as follows −Marketing/RetailingData mining can help direct marketers by supporting them with useful and accurate trends about their users purchasing behavior. It is based on these trends, marketers can direct their marketing attention to their customers with more precision. For example, marketers of a software company may advertise their new software to consumers who have a lot of software purchasing history.Moreover, data mining can also help marketers in predicting which products their users can be interested in purchasing. Through this prediction, marketers can surprise their users and create the users shopping ... Read More