Data mining joins computer science and statistics used to discover trends from the information bank. The main objective is to generate useful information from large data and mold it into understandable structures that can make key decisions and predictions for the future. Data mining algorithms have been used for many years, with many advancements projecting huge growth in data. Many firms have used different preferential approaches to data mining intensively and extensively in enabling business intelligence. These approaches include the C4.5 algorithm, K-means algorithm, Support vector machines algorithm, Apriori algorithm, EM algorithm, PageRank Algorithm, AdaBoost Algorithm, and kNN algorithm, as discussed. This paper will analyze these data mining algorithms and recommend why C4.5 is the algorithm to enable technology for business intelligence that can organize, search, and capture information through filters to create meaningful conclusions.
Data Mining Algorithms
Data mining is the extraction of meaningful information, trends, and patterns from a large data set. It is important because it helps develop smart market decisions, make accurate predictions, and analyze customer behaviors and practices across different industries. There are various types of data mining approaches used in the modern world of data. This paper will discuss preferential approaches to data mining.
The preferred approach to data mining for business intelligence is C4.5. This algorithm is used to generate a decision tree used for classification. K-means algorithm works by partitioning the dataset into distinct groups that do not overlap and belong to only one group. Support vector machines algorithm is useful for challenges involving both classification and regression analysis. In addition, another approach is the Apriori algorithm, an essential frequent set of mining and association of relational databases. Other approaches are the EM algorithm used in point estimation, PageRank Algorithm critical for ranking web pages, AdaBoost Algorithm used as an ensemble method in making mining easy. The kNN algorithm can be employed for both classification and regression towards predictive problems. This algorithm for data mining is majorly used for classifying predictive issues across different industries based on the data set.
C4.5 approach to data mining executes data in decision trees, making it easy to make decisions from the suitable form generated from the data. C4.5 helps organize data sets in easy ways to make decisions and note patterns as the data infer them. This approach is a form of data classifier that groups the data according to patterns and trends, making it easy to generate meaningful inferences from the data set (Rutkowski et al., 2020). This classification presented by C4.5 is suitable for decision-making since they easily show trends that the data owner can easily follow and judge from the data set. This algorithm is versatile and has relatively high accuracy as there are better-supervised learning models important for classification and regression.
An example of how C4.5 is employed is when a data set contains information on sales made by an organization listed concerning the products purchased and the different market segments. Therefore, this data can easily be classified according to varying sales in various regions in relation to the products. This makes it easy for one to understand the patterns and trends of how another product behaves best in multiple areas. This explains why C4.5 data mining is the best for capturing information that needs to be propagated through filters to help come up with logical conclusions from the data.
This paper recommends the C4.5 data mining approach to enable technology for business intelligence like organizing, capturing, and searching for information. C4.5 categorizes data into decision trees, making it easy to make sound decisions as the data set represented is already classified. In addition, classification done by this approach is vital in modeling and predicting variables based on the inherent structures of the data. However, the C4.5 algorithm has a limitation in that the algorithm does not permit numeric attributes to allow missing values and arbitrary approximate descriptions. Through the ID3 extension, the C4.5 algorithm can overcome this limitation since the training data set can be formed with numerical attributes, and handling such variables is suitable with C4.5.
Rutkowski, L., Jaworski, M., & Duda, P. (2020). Stream data mining: algorithms and their probabilistic properties. Cham, Switzerland: Springer.