Class discrimination is defined as classism. It is prejudice or discrimination based on social class. It involves individual attitudes, behaviors, systems of policies, and practices that are set up to benefit the upper class at the amount as the lower class.Classism can define personal prejudice against lower classes and institutional classism, just as the term racism can define either strictly to personal prejudice or institutional racism. The latter has been represented as how conscious or unconscious classism is clear in the several institutions of our society".Class discrimination can be viewed in several forms of media including television shows, films, and ... Read More

Data generalization summarizes data by replacing relatively low-level values (including numeric value for attribute age) with high-level concepts (including young, middle-aged, and senior). Therefore, it is a process that abstracts a huge set of task-relevant information in a database from a relatively low conceptual level to higher conceptual levels.Following are the two approaches for the efficient and flexible generalization of large data sets −OLAP approach − The data cube technology can be treated as a data warehouse-based, pre-computation-oriented, materialized view approach. It implements offline aggregation earlier an OLAP or data mining query is moved for processing.Attribute-oriented induction approach − It ... Read More

Agglomerative clustering is a bottom-up clustering method where clusters have subclusters, which in turn have sub-clusters, etc. It can start by placing each object in its cluster and then mix these atomic clusters into higher and higher clusters until all the objects are in an individual cluster or until it needs definite termination condition. Some hierarchical clustering methods used to this type. The distinct only in their description of between-cluster similarity.For example, a method called AGNES (Agglomerative Nesting), need the single-link techniques and operates as follows. Consider there are group of objects placed in a rectangle. Initially, every object is ... Read More

Association rule learning is a type of unsupervised learning methods that tests for the dependence of one data element on another data element and create appropriately so that it can be more effective. It tries to discover some interesting relations or relations among the variables of the dataset. It depends on several rules to find interesting relations between variables in the database.The association rule learning is the important technique of machine learning, and it is employed in Market Basket analysis, Web usage mining, continuous production, etc. In market basket analysis, it is adequate used by several big retailers to find ... Read More

Concept DescriptionConcept Description is a definitive type of data mining. It defines a set of data including frequent buyers, graduate candidates, etc. It describes the characterization and comparison of the data. It is also known as a class description when the concept to be described is defined as a class of objects. These descriptions can be determined with the support of data characterization.Data characterization is a summarization of the general characteristics of the target class of data. The data relating to a specific user-defined class is usually recovered by a database query. The output of data characterization can be presented ... Read More

Market basket analysis does not represent an individual method and it represents a set of business issues related to understanding point-of-sale transaction data. Market basket data is transaction data that represents three fundamentally different entities such as users, orders (also called purchases or baskets or, in academic papers, item sets) and Items.The order is the component data structure for market basket data. An order describe a single purchase event by a user. This can correlate to a user ordering several products on a website or to users buying a basket of groceries or to a user buying a several items ... Read More

KDD represents Knowledge Discovery in Databases. It defines the broad process of discovering knowledge in data and emphasizes the high-level applications of definite data mining techniques. It is an area of interest to researchers in several fields, such as artificial intelligence, machine learning, pattern recognition, databases, statistics, knowledge acquisition for professional systems, and data visualization.The main objective of the KDD process is to extract data from information in the context of huge databases. It does this by utilizing Data Mining algorithms to recognize what is considered knowledge.The Knowledge Discovery in Databases is treated as a programmed, exploratory analysis and modeling ... Read More

Association rule learning is a type of unsupervised learning methods that tests for the dependence of one data element on another data element and create appropriately so that it can be more effective. It tries to discover some interesting relations or relations among the variables of the dataset. It depends on several rules to find interesting relations between variables in the database.The association rule learning is the important technique of machine learning, and it is employed in Market Basket analysis, Web usage mining, continuous production, etc. In market basket analysis, it is an adequate used by several big retailers to ... Read More

Data Mining 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 an interdisciplinary field, the assemblage of a set of disciplines, such as database systems, statistics, machine learning, visualization, and data science. It is based on the data mining methods used, approaches from other disciplines can be used, including neural networks, fuzzy and rough set theory, knowledge representation, inductive logic programming, or high-performance computing.It is established on the types of data to ... Read More

Collaborative filtering is a different of memory-based reasoning especially well appropriated to the application of supporting personalized recommendations. A collaborative filtering system begins with a history of person preferences. The distance function decides similarity depends on overlap of preferences persons who like the same thing are close.Furthermore, votes are weighted by distances, therefore the votes of closer neighbors count more for the endorsement. In another terms, it is an approach for discovering music, books, wine, or someone else that fits into the current preferences of a specific person by using the judgments of a peer group choose for their same ... Read More