Advancement in the field of Information Technology has to lead to a large number of databases in various areas. As a result, there is a store and manipulate the data that will later for decision making. Data Mining Projects is a set of calculations that create a model from data. To create a model, the algorithm analyzes the data you provide and looking for specific patterns.
In fact, it is a process that involves the retrieval of all types of data from various sources. Data will analyze and crawl through to retrieve relevant information from data sources in a specific pattern.
List of Data Mining Algorithms
C4.5 – C4.5 algorithm will represent as a decision tree classifier that builds decision trees from a set of data in the same way. It can generate a decision depend on a sample of data.
K means – As well as, it is a clustering algorithm and machine learning tool that is utilized to cluster observations into groups of observations. One of the simple clustering techniques.
Apriori – It is an influential algorithm for mining frequent itemsets and relevant association for boolean rules.
Expectation – Maximization – is like clustering algorithms which iterate and optimizes the parameters in statistical models.
PageRank – In fact, it is a link analyzing algorithm will use to find the relative importance of object linked within a network object.
AdaBoost – A boosting algorithm that constructs a classifier. It can be used in composition with other types of learning algorithms to improve performance.
KNN – K-Nearest Neighbors algorithm may refer to as a classification algorithm. It is used for both classification and regression.
Constraints Based Data Mining Algorithms
Constraints play a major role in data mining. It is a broad field incorporate with many different kinds of techniques for discovering and new knowledge from data. In general, the data mining tasks such as clustering, pattern mining, Big Data classification, and detection. Each data mining task provides a set of popular algorithms. additionally, these algorithms may define to handle a general and simple case which will apply in many domains. The Constraint helps to focus the search or mining process and attenuate the computational time.
There are two ways to integrate constraints in data mining algorithms.
• It is possible to add constraints by performing post-processing on the result of the data mining algorithm
• As the matter of fact, it is possible to add constraints directly in algorithms to improve the efficiency of the algorithms. Hence it is easy to implement and provide better performance.
In the form of constraints, it reduces the hypothesis space. As well as, it can reduce the processing time and improve the learning quality. In the final analysis, it is an important tool in several areas of business and the techniques in deriving a solution to a problem.