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

Data mining also defined as Knowledge Discovery in Data is a technique to recognize any anomalies, correlations, trends, or patterns between millions of data (especially structured data) to glean insights that can be useful for business decision making and might have been missed during traditional analysis. The objective of data mining is to find facts or data that was previously ignored or not known using complicated numerical algorithms.Data Mining is similar to Data Science. It is carried out by a person, in a particular situation, on a specific data set, with an objective. This phase contains several types of services ... Read More

Distance is the method the MBR computes similarity. For some true distance metric, the distance from point A to point B, indicated by d(A, B), has four features which are as follows −Well-defined − The distance among two points is continually defined and is a nonnegative real number, d (A, B) ≥ 0.Identity − The distance from one point to itself is continually zero, so d (A, A) = 0.Commutativity − Direction does not create a difference, therefore the distance from A to B is the similar as the distance from B to A: d(A, B) = d(B, A). This ... 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.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 depending on the data mining approach used, techniques from other disciplines may be ... Read More

There are various elements of MBR which are as follows −Choosing the Training Set − The training set included 49, 652 news stories, supported by the news retrieval service for this goal. These stories appears from about three months of news and from almost 100 multiple sources.Each story included, on average, 2, 700 words and had eight codes created to it. The training set was not particularly created, therefore the frequency of codes in the training set varied a big deal, mimicking the complete frequency of codes in news stories in general.Choosing the Distance Function − The next phase is ... Read More