A Survey On Data Mining Classification Algorithms

48) Generally speaking, data mining tasks can be classified into three main categories: ________, association, and clustering.

Data mining allows companies to focus on the more important information in their data warehouses. Data mining can be broken down into two major categories. Automated prediction of trends and behaviors, and automated discovery of previously unknown patterns. In the first category, data mining automates the process of finding predictive information in large databases. Questions that traditionally required exhaustive hands-on analysis can now be quickly answered directly from data. In the second category, data mining tools sweep through databases and identify previously hidden patterns in one step. This category is where the major focus of research has been on.

Nowadays, data mining and machine learning become rapidly growing topics in both industry and academic areas. Companies, government laborites and top universities are all contributing in knowledge discovery of pattern recognition, text categorization, data clustering, classification prediction and more. In general, data mining is the technique used to analyze data from multi perspectives and reveal the hidden gem behind the enormous amount of data. With the explosive growth of data collections, it becomes time-consuming less effective to extract valuable information from massive databases through the use of traditional data analysis methods. An alternative way to solve this problem is to apply data mining, given considerations

DATA MINING: means searching and analyzing large masses of data to discover patterns and develop new information.

Usually the data mining analysis is done by grouping commonly co-occuring things (Associations), discovering time-ordered events (Sequences), anticipating future occurences (Predictions), identifying natural groupings of items (Clusters) and finally, by uncovering generalizations to help classify items (Classification). These different type of mining usually take a lot of time and a good understanding of the business and

Data management, mining, and warehousing all deal with data in different ways. Data management establishes the groundwork for an organization to structure, regulate, process, and store data that they acquire (Rouse, 2016). Data management also encompasses the creation of definitions and standards for the acquired data which will be adhered to throughout the organization (Definition of: Data management, 2016).

Data Mining, a sub-branch of computer science, involving statistics, methods and calculations to find patterns in large amount of data sets, and database systems. Generally, data mining is the process to examine data from different aspects and summarizing it into meaningful information. Data mining techniques depict actions and future trends, allowing any individual to make better and knowledge-driven decisions.[1][2]

It is one of the biggest challenges in data mining. It is a predictive modeling task with the specific aim of predicting the value of a single nominal variable based on the known values of other variables. Classification is the task of generalizing known structure to apply to new data. 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. A classification task begins with a data set in which the class assignments are known. Classifications are discrete and do not imply order. Continuous, floating-point values would indicate a numerical, rather than a categorical, target. A predictive model with a numerical target uses a regression algorithm, not a classification algorithm. The simplest type of classification problem is binary classification. In binary classification, the target attribute has only two possible values: for example, high credit rating or low credit rating. Multiclass targets have more than two values: for example, low, medium, high, or unknown credit rating the model build (training) process, a classification algorithm finds relationships between the values of the predictors and the values of the target. Different classification algorithms use different techniques for finding relationships. These relationships are summarized in a model, which can then be

Data mining techniques are basically categorised into two major groups as Supervised learning and Unsupervised learning. Clustering is a process of grouping the similar data sets into groups. These groups should have two properties like dissimilarity between the groups and similarity within the group. Clustering is covered in the unsupervised learning category. There are no predefined class label

it is the most effective data mining technique to discover hidden or desired pattern among the large amount of data. It is responsible to find correlation relationships among different data attributes in a large set of items in a database .

Traditionally Data Mining is a process of extracting useful knowledge from a large volume of data set. The generated knowledge is applied and used for the most of the applications in all the areas such as science, engineering, business, research, social, health, education, entertainment and all. As all the

Huge amounts of data are collected nowadays from different application domains and are not feasible to analyze all these data manually. Knowledge Discovery in Databases (KDD) is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. In recent years, soft computing became more and more attractive for the researchers, who work in the related research field of data mining. This paper concerns primarily about how to use soft computing model to extract knowledge from data mining (database). The data mining preprocessing techniques have been applied on the available data to clean it in proper form to extract the knowledge from the data. Thereafter, statistical analysis and soft computing techniques have been applied on the clean data to select the preferable model. The decision of the preferable model has to be achieved based on the maximum number of minimum value of the parameters of residual analysis and average error. The preferable model has been used to extract the knowledge from the data mining. The goal of the

Data Mining technique is the result of a long process of studies and research in the area of databases and product development. This evolution began when business data and companies was stored for the first time on computer device, with continuous improvements in access to data and more newly, produced technologies that allow users to navigate during their data in real time. Data mining is a approach that help to mine important data from a large database. It is the technique of classification during huge amounts of data and chosen out relevant information during the use of certain advanced algorithms. Like more data is collected, with the amount of data doubling every one years, data mining is becoming an more and more important tool to convert this data into information. Data mining takes this evolutionary process behind retrospective data access and navigation to prospective and proactive information delivery. Data mining is very useful and ready in applications in the business

Data mining is a detection process that allows users to comprehend the substance and relationships amid the data. Data architect/designer punctiliously defines entities and relationships from operational or data warehouse system. The conclusion of data mining can be used to intensify the efficacy of performance from the users. Data mining uses various techniques such as inductive logic programming, pattern recognition, image analysis, bioinformatics, spatial data analysis, decision support systems etc. for this sort of analysis.

The overall goal of the data mining process is to extract information from data sets and transform it into an understandable structure such as patterns and knowledge for further use [3].