Data mining refers to extracting or mining knowledge from large amounts of data. In other words, Data mining is the science, art, and technology of discovering large and complex bodies of data in order to discover useful patterns.
Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks.
Data mining treat as a synonym for another popularly used term, Knowledge Discovery from Data, or KDD. In others view data mining as simply an essential step in the process of knowledge discovery, in which intelligent methods are applied in order to extract data patterns.
Prediction: This technique specifies the relationship between independent and dependent instances. For example, while considering sales data, if we want to predict the future profit, the sale acts as a separate instance, whereas the payoff is the dependent instance. Accordingly, based on sales and profit's historical data, the associated profit is the predicted value.
Decision trees: It specifies a tree structure where the decision tree's root acts as a condition/question having multiple answers. Each answer sets to specific data that helps in determining the final decision based on the data.
Clustering analysis: This technique specifies that a cluster of objects having similar characteristics is formed automatically. The clustering method defines classes and then places suitable objects in each class.
Sequential Patterns: This technique is used to specify the pattern analysis used for discovering identical patterns in transaction data or regular events. For example, customers' historical data helps a brand identify the patterns in the transactions that happened in the past year.
Classification Analysis: This is a Machine Learning based method in which each item in a particular set is classified into predefined groups. It uses advanced techniques like linear programming, neural networks, decision trees, etc.
Association rule learning: This technique is used to create a pattern based on the items' relationship in a single transaction.
Prediction can be viewed as the construction and use of a model to assess the class of an unlabeled object, or to measure the value or value ranges of an attribute that a given object is likely to have. In this interpretation, classification and regression are the two major types of prediction problems where classification is used to predict discrete or nominal values, while regression is used to predict incessant or ordered values.
A Bayesian classifier is a statistical classifier. They can predict class membership probabilities, for instance, the probability that a given sample belongs to a particular class. Bayesian classification is created on the Bayes theorem. A simple Bayesian classifier is known as the naive Bayesian classifier to be comparable in performance with decision trees and neural network classifiers. Bayesian classifiers have also displayed high accuracy and speed when applied to large databases.
A neural network is a set of connected input/output units where each connection has a weight associated with it. During the knowledge phase, the network acquires by adjusting the weights to be able to predict the correct class label of the input samples. Neural network learning is also denoted as connectionist learning due to the connections between units. Neural networks involve long training times and are therefore more appropriate for applications where this is feasible. They require a number of parameters that are typically best determined empirically, such as the network topology or “structure”. Neural networks have been criticized for their poor interpretability since it is difficult for humans to take the symbolic meaning behind the learned weights. These features firstly made neural networks less desirable for data mining.
In Data Mining, the clustering algorithm is used to group sets of data with similar characteristics (also known as clusters). By the use of these clusters, we can make faster decisions and explore data. First, this algorithm identifies the relationships in a dataset, and then it generates a series of clusters based on the relationships. The process of creating clusters is also repetitive.
DMX is an acronym that stands for Data Mining Extensions. It is a query language for Data Mining models supported by Microsoft's SQL Server Analysis Services product. Same as SQL also supports a data definition language, data manipulation language, and a data query language, all three with SQL-like syntax.
Genetic algorithm is a part of evolutionary computing which is a rapidly growing area of artificial intelligence. The genetic algorithm is inspired by Darwin’s theory about evolution. Here the solution to a problem solved by the genetic algorithm is evolved. In a genetic algorithm, a population of strings (called chromosomes or the genotype of the gen me), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, is evolved toward better solutions. Traditionally, solutions are represented in the form of binary strings, composed of 0s and 1s, the same way other encoding schemes can also be applied.
Association analysis is the finding of association rules showing attribute-value conditions that occur frequently together in a given set of data. Association analysis is widely used for a market basket or transaction data analysis. Association rule mining is a significant and exceptionally dynamic area of data mining research. One method of association-based classification, called associative classification, consists of two steps. In the main step, association instructions are generated using a modified version of the standard association rule mining algorithm known as Apriori. The second step constructs a classifier based on the association rules discovered.
When a decision tree is built, many of the branches will reflect anomalies in the training data due to noise or outliers. Tree pruning methods address this problem of over-fitting the data. So the tree pruning is a technique that removes the overfitting problem. Such methods typically use statistical measures to remove the least reliable branches, generally resulting in faster classification and an improvement in the ability of the tree to correctly classify independent test data. The pruning phase eliminates some of the lower branches and nodes to improve their performance. Processing the pruned tree to improve understandability.
Chameleon is another hierarchical clustering technique that utilization dynamic modeling. Chameleon is acquainted with recover the disadvantages of the CURE clustering technique. In this technique, two groups are combined, if the interconnectivity between two clusters is greater than the inter-connectivity between the object inside a cluster/ group.
K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problems. K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster.
Among numerous differences, the significant difference between PCA and FA is that factor analysis is utilized to determine and work with the variance between variables, but the point of PCA is to explain the covariance between the current segments or variables.
In Data Mining, we have to deal with mainly two things, database size, and query complexity.