Data Mining Algorithms: Applications

Subject: Tech & Engineering
Pages: 3
Words: 921
Reading time:
4 min
Study level: College

Over the years, technological advancement and the competitive business world have necessitated the development of various data mining algorithms. These different techniques have proved useful in obtaining important data from large sets of raw data. Data mining, therefore, is the processes through which different algorithms and software are used to search organize, and capture data that is useful in drawing valid conclusions. Although there are several data mining algorithms, the C4.5 algorithm is an easy and accurate data mining algorithm that helps organizations come up with valid conclusions as it makes use of decision trees to narrow down to conclusions. Therefore, this essay expounds on the C4.5 data mining algorithm while at the same time explaining why it is a preferential data mining approach. In addition, the essay will also highlight other data mining techniques although in brief to help make valuable comparisons.

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Data Mining Algorithms

In the recent past, data mining has emerged as an important aspect for businesses in the processes of decision-making. Data mining has become a lucrative multibillion-dollar industry and organizations are investing huge sums of money to ensure that they acquire relevant information regarding their client behaviors. Overall, data mining has been used in all spheres of life to study, understand and come up with viable solutions to problems. Therefore, this essay envisions and describes in detail the C4.5 algorithm as a preferential data mining approach that can be used as an enabling technology for business intelligence that would lead to drawing legitimate conclusions.

Various data mining algorithms backed by artificial intelligence have been developed to meet the rising need to make accurate and legitimate business decisions. The most commonly used data mining algorithms include the following; first, the C4.5 algorithm is a supervised data mining tool that makes use of decision trees to classify data sets, and as such it is at times referred to as a classifier algorithm. Secondly, Kaur and Gangwar (2017) argue that the K-means algorithm is one of the simplest unsupervised learning algorithms in data mining. The algorithm is used in identifying unlabeled groups of data in the raw data set. Therefore, the algorithm is not ideal for making conclusions as it gives grouped results.

Julianto et al. (2021) explain that the Support vector machines (SVM) algorithm is a supervised machine learning algorithm that is crucial in the classification of data sets. Apriori is another crucial data algorithm that uses common items in a data set to come up with grouping rules. According to Guo et al. (2017), this algorithm is ideal for use in transaction databases to come up with items that record a high number of transactions.

According to Han et al. (2020), the Page Rank algorithm is an algorithm that is used to rank website pages to determine their level of importance. This algorithm is important for companies that are interested in determining the amount of traffic generated by website pages. The algorithm can rank the websites according to their levels of importance. Page Rank algorithm is crucial for organizations in determining which marketing strategy to adopt.

C4.5 Data Mining Algorithm

According to Julianto et al. (2021), data mining is an important business aspect that has proven crucial in decision-making. Although several algorithms have been developed to carry out efficient data mining activities, C4.5 has proven crucial in organizations for decision-making compared to other data mining algorithms. C4.5 algorithm is an excellent algorithm as it makes use of decision trees to generate decisions that are legible and easy to understand for human beings. According to Sousa et al. (2017), C4.5 achieves this by constructing a classifier in the form of a decision tree from the given data set. The decision trees are crucial in creating flowcharts that are useful in classifying new data. However, it is important to note that the C4.5 algorithm is a supervised machine learning tool as it makes use of classified data sets. The algorithm does not use its intelligence to come up with decisions but rather relies on classified information that is first fed into the system.

In addition, the C4.5 data mining algorithm differs principally from other data mining algorithms through its mode of operation. Although other data mining algorithms carry out pruning, C4.5 uses a single-pass pruning mechanism to make accurate conclusions. Single-pass pruning results in outcomes that are highly accurate and informative thereby helping in meaningful decision-making processes. Julianto et al. (2021) argue that the algorithm is not constrained by the nature of the data set available. Data sets to be sieved by algorithms are either continuous or discrete. Interestingly, C4.5 can work on any data set thereby setting it apart from other data mining algorithms. For continuous data sets, the algorithm can convert it into discrete data thereby making it usable in decision trees providing accurate outcomes. Lastly, the C4.5 algorithm can sort out incomplete data sets using an inbuilt mechanism thereby avoiding giving misleading responses.

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Overall, the C4.5 data mining algorithm is ideal as a preferential data mining approach that can be used as an enabling technology for business intelligence, such that organizing, searching, and capturing information can be propagated through filters that would lead to drawing legitimate conclusions. Nonetheless, the C4.5 algorithm is fast in executing operations compared to other data mining algorithms available. Lastly, the fact that the C4.5 algorithm executes information in the form of decision trees further makes it ideal for data mining. This is because it helps narrow down the available information by using known patterns to arrive at legitimate and accurate conclusions.

References

Guo, Y., Wang, M., & Li, X. (2017). Application of an improved Apriori algorithm in a mobile e-commerce recommendation system. Industrial Management & Data Systems 2(7), 29-278. Web.

Han, C., Fang, M., Ma, T., Cao, H., & Peng, H. (2020). An intelligent decision-making framework for asphalt pavement maintenance using the clustering-PageRank algorithm. Engineering Optimization, 52(11), 129-847. Web.

Julianto, I. T., Rohmanto, R., Sarifudin, U., & Widianto, S. R. (2021). Performance comparison of data mining algorithms which occupy the top: C4. 5 and SVM. Jurnal Mantik, 4(4), 299-507. Web.

Kaur, R., & Gangwar, R. (2017). A review on Naive Baye’s (NB), J48 and K-means based mining algorithms for medical data mining. Int. Res. J. Eng. Technol, 4, 164-668.

Sousa, T., Silva, A., & Neves, A. (2017). Particle swarm based data mining algorithms for classification tasks. Parallel computing, 30(5-6), 67-783: Web.