A Survey on Data Mining Classification Algorithms
Abstract: Classification is one of the most familiar data mining technique and model finding process that is used for transmission the data into different classes according to particular condition. Further the classification is used to forecast group relationship for precise data instance. It is generally construct models that are used to predict potential statistics trends. The major objective of machine data is to perfectly predict the class for each record. This article focuses on a survey on different classification techniques that are mostly used in data-mining.
Keywords: Data mining, Classification, decision tree, neural network.
1. INTRODUCTION
Data mining is one of the many
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Classification contains finding rules that partition the data into disjoint groups patterns and process. The goal of classification is to evaluate the input data to develop a precise. Explanation or model for each class using the features by using the present data.
2. ARCHITECTURE OF DATA MINING
Data mining and knowledge discovery is the name frequently used to refer to a very interdisciplinary field, which consists of using methods of several research areas to extract knowledge from real-world datasets. There is a distinction between the terms data mining and knowledge discovery which seems to have been introduced by [Fayyad et al.1996].the term data mining refers to the core step of a broader process, called knowledge discovery in database. Architecture of data mining structure is defined the following figure.
3. DATA MINING PROCESS
Data cleaning
Data integration
Data selection
Data transformation
Pattern evaluation
Knowledge presentation.
Data cleaning: Data cleaning or data scrubbing is the process of detecting as well as correcting (or removing) inaccurate data from a record set. It handles noisy data it represents random error in attribute values. In very large dataset noise can come in many shapes and forms. And irrelevant data handles the missing and unnecessary data in the source file.
Data integration: Data integration process contains the data from multiple sources.
Data Mining is an analytical process that primarily involves searching through vast amounts of data to spot useful, but initially undiscovered, patterns. The data mining process typically involves three major stepsexploration, model building and validation and finally, deployment.
What is data mining? Data mining is the deriving new information from massive amounts of data in databases (Sauter, 2014, p. 148). Chowdhurry argues that data mining is part of KDD. KDD is knowledge discovery in databases, it is a process that includes data mining. In addition to data mining, KDD includes data preparation, modeling and evaluation of KDD. KDD is at the heart of this research field. This research field is multidisciplinary and includes data visualization, machine learning, database technology, expert systems and statistics. Overall, the use of a case based reasoning and data mining tools within an information system would create a CBR system to solve new problems with adapted solutions and could be used in many industries such as education and healthcare (Chowdhurry,
4) Technically speaking, data mining is a process that uses statistical, mathematical, and artificial intelligence techniques to extract and identify useful information and
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]
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
Today with the ever growing use of computers in the world, information is constantly moving from one place to another. What is this information, who is it about, and who is using it will be discussed in the following paper. The collecting, interpreting, and determination of use of this information has come to be known as data mining. This term known as data mining has been around only for a short time but the actual collection of data has been happening for centuries. The following paragraph will give a brief description of this history of data collection.
Data mining: is a process of discovering patterns in large data involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems with the aim of extracting information and transforming it into an understandable structure for further use.
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
Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any business in today’s market. Every day these facilities store large amounts of data to improve increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible future trends; say for seasonal reasons. Data
Data mining is really just the next step in the process of analyzing data. Instead of getting queries on standard or user-specified relationships, data mining goes a step farther by finding meaningful relationships in data. Relationships that were thought to have not existed, or ones that give a more insightful view of the
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].
Data mining is “[t]he process of finding significant, previously unknown, and potentially valuable knowledge hidden in data” (Gordon, 2007). Organizations use data mining to sift through massive quantities of raw data in order to find patterns and relationships that will ultimately be used for business purposes (Definition of: Data mining, 2016). Organizations mainly use data mining to get a better idea of their customer’s purchasing habits, product preferences, etc. in order to create sales tactics targeted at a certain customer demographic (Definition of: Data management, 2016).
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
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
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.