Found 427 Articles for Data Mining

Generalized linear models defines the theoretical authority on which linear regression can be used to the modeling of categorical response variables. In generalized linear models, the variance of the response variable, y, is a function of the mean value of y, unlike in linear regression, where the variance of y is constant.Generalized linear models (GLMs) are an expansion of traditional linear models. This algorithm fits generalized linear models to the information by maximizing the loglikelihood. The elastic net penalty can be used for parameter regularization. The model fitting calculation is parallel, completely fast, and scales completely well for models with ... Read More

CBR stands for Case-based reasoning. CBR classifiers need a database of problem solutions to clarify new problems. Unlike nearest-neighbor classifiers, which save training tuples as points in Euclidean space, CBR saves the tuples or “cases” for problem solving as difficult symbolic representation.There are various business applications of CBR include problem resolution for customer service help desks, where cases describe product-related diagnostic problems. CBR has been used to areas including engineering and law, where cases are technical designs or legal rulings, accordingly.Medical education is an application for CBR, where patient case histories and treatments are used to support diagnose and consider ... Read More

Backpropagation defines the whole procedure encompassing both the computation of the gradient and its need in the stochastic gradient descent. Technically, backpropagation is used to calculate the gradient of the error of the network with respect to the network’s modifiable weights.The characteristics of Backpropagation are the iterative, recursive and effective approach through which it computes the updated weight to enhance the network until it cannot perform the function for which it is being trained. Derivatives of the activation service to be known at web design time is needed to Backpropagation.Backpropagation is generally used in neural network training and computes the ... Read More

Bayesian classifiers are statistical classifiers. They can predict class membership probabilities, including the probability that a given sample belongs to a specific class. Bayesian classifiers have also display large efficiency and speed when it can high databases.Once classes are defined, the system should infer rules that govern the classification, therefore the system should be able to find the description of each class. The descriptions should only refer to the predicting attributes of the training set so that only the positive examples should satisfy the description, not the negative examples. A rule is said to be correct if its description covers ... Read More

An attribute selection measure is a heuristic for choosing the splitting test that “best” separates a given data partition, D, of class-labeled training tuples into single classes.If it can split D into smaller partitions as per the results of the splitting criterion, ideally every partition can be pure (i.e., some tuples that fall into a given partition can belong to the same class).Conceptually, the “best” splitting criterion is the most approximately results in such a method. Attribute selection measures are called a splitting rules because they decides how the tuples at a given node are to be divided.The attribute selection ... Read More

Decision tree induction is the learning of decision trees from class-labeled training tuples. A decision tree is a sequential diagram-like tree structure, where every internal node (non-leaf node) indicates a test on an attribute, each branch defines a result of the test, and each leaf node (or terminal node) influence a class label. The highest node in a tree is the root node.It defines the concept buys computer, i.e., it predicts whether a user at AllElectronics is likely to buy a computer. Internal nodes are indicated by rectangles, and leaf nodes are indicated by ovals. There are various decision tree ... Read More

Classification is a data-mining approaches that assigns elements to a set of data to aid in more efficient predictions and analysis. The classification is generally used when there are two target classes known as binary classification.When higher than two classes can be predicted, especially in pattern recognition problems, this is defined as multinomial classification. However, multinomial classification can be used for categorical response data, where one needs to predict which category amongst various elements has the instances with the largest probability.Data classification is a two-step phase. In the first phase, a classifier is built defining a predetermined collection of data ... Read More

Rule constraints can be classified into the following five elements which are as follows −Antimonotonic − The first elements of constraints is antimonotonic. Consider the rule constraint “sum (I.price) ≤ 100”. Consider that it is using the Apriori framework, which at every iteration k analyze itemsets of size k. If the cost summation of the items in an itemset is no less than 100, this itemset can be shorten from the search space, because inserting more items into the set will only create it more costly and therefore will not satisfy the constraint.Pruning by antimonotonic constraints can be used at ... Read More

Data mining is the process of finding useful new correlations, patterns, and trends by transferring through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.It is the procedure of selection, exploration, and modeling of high quantities of information to find regularities or relations that are at first unknown to obtain clear and beneficial results for the owner of the database.Data Mining is similar ... Read More

A data mining procedure can uncover thousands of rules from a given set of information, most of which end up being independent or tedious to the users. Users have a best sense of which “direction” of mining can lead to interesting patterns and the “form” of the patterns or rules they can like to discover.Therefore, a good heuristic is to have the users defines such intuition or expectations as constraints to constraint the search space. This strategy is called constraint-based mining.Constraint-based algorithms need constraints to decrease the search area in the frequent itemset generation step (the association rule generating step ... Read More