Found 422 Articles for Data Mining

There are various methods of clustering which are as follows −Partitioning Methods − Given a database of n objects or data tuples, a partitioning method assembles k partitions of the information, where each partition defines a cluster, and k < n. It can allocate the data into k groups, which can satisfy the following necessity −Each group must include a minimum of one object.Each object should apply to accurately one group.Given k, the number of partitions to construct, a partitioning method makes an initial partitioning. It then uses an iterative relocation method which attempts to improve the partitioning by transforming ... Read More

There are various applications of clustering which are as follows −Scalability − Some clustering algorithms work well in small data sets including less than 200 data objects; however, a huge database can include millions of objects. Clustering on a sample of a given huge data set can lead to biased results. There are highly scalable clustering algorithms are required.Ability to deal with different types of attributes − Some algorithms are designed to cluster interval-based (numerical) records. However, applications can require clustering several types of data, including binary, categorical (nominal), and ordinal data, or a combination of these data types.Discovery of ... Read More

There are various challenges of data mining which are as follows −Efficiency and scalability of data mining algorithms − It can effectively extract data from a large amount of data in databases, the knowledge discovery algorithms should be efficient and scalable to huge databases. Specifically, the running time of a data mining algorithm should be predictable and acceptable in huge databases. Algorithms with exponential or even channel-order polynomial complexity will not be of efficient use.Usefulness, certainty, and expressiveness of data mining results − The identified knowledge should exactly portray the contents of the database and be beneficial for specific applications. ... 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 systems are designed to promote the identification and classification of individuals into different groups or segments. From the aspect of the commercial firm, and possibly for the industry as a whole, it can interpret the use of ... Read More

Data mining is one of the forms of artificial intelligence that uses perception models, analytical models, and multiple algorithms to simulate the techniques of the human brain. Data mining supports machines to take human decisions and create human choices.The user of the data mining tools will have to direct the machine rules, preferences, and even experiences to have decision support data mining metrics are as follows −Usefulness − Usefulness involves several metrics that tell us whether the model provides useful data. For instance, a data mining model that correlates save the location with sales can be both accurate and reliable, ... 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 knowledge discovery process is iterative and interactive, includes nine steps. The process is iterative at every stage, implying that transforming back to the previous actions can be required. The process has several imaginative methods in the sense that one cannot present one formula or create ... 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 ... 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

The traditional techniques of turning data into knowledge depend on manual analysis and interpretation. For instance, in the healthcare industry, it is familiar for specialists to systematically analyze current trends and changes in healthcare data, every quarter.The specialists support a report detailing the analysis to the sponsoring healthcare organization; this report becomes the basis for future decision making and planning for healthcare management. There are several types of applications, including planetary geologists sifting through remotely sensed images of planets and asteroids, carefully situating and cataloging such geologic objects of interest as impact craters.This form of manual probing of a data ... Read More

Data Warehousing is a technique that is generally used to collect and manage data from multiple sources to provide the business a meaningful business insight. A data warehouse is specifically created for the goals of support management decisions.In simple terms, a data warehouse defines a database that is maintained independently from an organization’s operational databases. Data warehouse systems enable the integration of several application systems. They provide data processing by supporting a solid platform of consolidated, historical data for analysis.There are various types of data warehouse users which are as follows −Statisticians − There are generally only a handful of ... Read More