Research Paper on Current Developments in Data Mining, Machine Learning Techniques

Introduction

The advent of information technology has influenced various activities in the society such as banking, land records, libraries, and population data. Technological advancements have led to the storage of data in multiple forms for future reference. Companies such as Google and Facebook deal with a massive chunk of data, which is supposed to be stored safely and retrievable when needed. Data mining and machine learning are some of the essential techniques in information technology that enable the classification and storage of data in different formats. Battula and Prasad (2013) define data mining as the process of observing and analyzing data sets to detect unsuspected relationships, summarize the data, and presents it in novel ways that are understandable to the relevant parties. Machine learning, on the other hand, is the technology that enables computational systems to learn from stored data how to perform particular activities without human presence. Machine learning performs various tasks such as prediction, forecasting, and decision making (Ali, Qadir, Rasool, Sathiaseelan, & Zwitter, 2016). Traditional data mining and machine learning techniques do exist; however, there are more novel and efficient data mining and machine learning techniques that have been developed and applied by engineers.

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The Recent Machine Learning and Data Mining Techniques

Basic and traditional machine learning techniques include classification and tree diagrams. With the advent of information technology, more advanced and recent technologies have been designed to ease machine learning. One of the novel machine learning techniques is the hybrid active learning method. The approach involves sample selection from purely unsupervised criteria obtained from different clusters. The second phase of the process consists updating the trained classifiers with the relevant samples to push the classifier to a more predictive power (Battula & Prasad, 2017). Hybrid active learning reduces the annotation and supervision efforts of operators in both off-line and online classification systems.

Another recent machine learning technique is the kernel-based learning. The method increases computational capability based on the design of efficient nonlinear algorithms (Qiu et al., 2016). Kernel methods function by mapping samples from the source into an infinite-dimensional feature space to be calculated through a kernel function. The kernel method provides elegant mathematical solutions for constructing strong nonlinear variants derived from statistical learning techniques. Gaussian kernels and Polynomial kernels are the two widely used kernel functions. Distributed and parallel learning is another recent machine learning technique. Distributed learning avoids the method of assembling data into a single workstation for processing. The technique saves time and energy required to analyze and process data. Traditional machine learning algorithms were designed to train and test data obtained from the same feature space and distribution. With data originating from different sources, it caused heterogeneity, which destroyed the hypothesis. Transfer learning is the new methodology that tackles this issue by allowing domains and tasks to be distinct to extract knowledge from different activities and apply it to a specific function (Qiu et al., 2016).

Deep learning is the most recent technique used in machine learning. Deep learning utilizes both supervised and unsupervised methodologies in deep architectures to learn hierarchical representations automatically. Deep structures have the capability to capture complicated statistical patterns than traditional learning methods. The technique also executes actions faster than handmade features.

Recent Data Mining Techniques

Some of the well-known data mining techniques include statistical approaches and machine learning approaches through conceptual learning, inductive concept learning, and decision tree induction (Gheware, Kejkar, & Tondare, 2014). Some of the statistical techniques used in data mining include Bayesian networks, regression analysis, and correlation analysis. Equally important, NoSQL is one of the recent data mining technique that obtains unstructured data such as social media data, word documents, and blog posts and organizes the raw data to a structured fashion that is understandable. Companies such as Amazon and Google have adopted the use of NoSQL databases to arrange and save data. NoSQL databases are fast, scalable, and flexible than previous data mining techniques. Besides, predictive analytics is the other latest techniques used in data mining. Predictive analytics predicts future occurrences or behavior by relying on past experiences. Predictive analysis functions by combining machine learning, data science, and statistical modeling. The technique provides empirical predictions based on the inputs in the empirical data.

Applications of both Data Mining and Machine Learning Techniques

Data mining and machine learning are two methods that work interchangeably to ensure data is obtained and interpreted in an understandable manner. Both approaches have applications in various sectors of the economy. Data mining is used in financial data analysis. The use of data mining techniques to obtain financial data helps banking institutions and other financial industries to design data warehouses for multidimensional data analysis. Data mining in the banking industry is also used for loan payment prediction and customer credit policy analysis (Gheware, Kejkar, & Tondare, 2014). Data mining is also used in the detection of financial crimes such as money laundering. Moreover, in the retail industry, data mining collects various types of record from goods transportation, customer-purchasing history, and many others. According to research by Silwattananusarn and KulthidaTuamsuk (2012), the hybrid

SOFM/LVQ classifier and the financial knowledge management system (FKMS) played a significant role in the clustering and classifying of corporate bonds. Data mining helps the retail sector to recognize customer buying patterns and trends; thus, assists the industry to improve customer service, satisfaction, and retention. Data mining also has applications in the telecommunication industry. Data mining is used to identify fraudulent activities, telecommunication patterns, and improve the efficiency of services to the customers. Data mining has significant applications in the biological field. It is used in the similarity search, alignment, indexing, and comparative analysis of multiple nucleotide sequences.

Data mining is used in web organization; a term referred to as the semantic web. The web organization capability goes further to the business organization. Data mining techniques of predicting and clustering are currently utilized by a significant number of businesses to help in business decision making and firm business intelligence (BI) systems (Kumar & Bhardwaj, 2011). Additionally, data mining is used in security and data privacy. Data mining offers an evaluation of the impacts of social groups and dynamics. Moreover, the classification component of data mining in the healthcare system enables the classification of patients from primary healthcare centers to specialists.

Data mining together with machine learning are used in the fight against terrorism. After 911 attack, the United States introduced the Total Information Awareness program that was designed to collect all information regarding the population. The program managed to gather data with the help of data mining and machine learning. Both data mining and machine learning have also been used for cybersecurity intrusion detection (Buczak, 2016). Machine learning approaches such as artificial neural networks (ANNs). Automatic neural networks are used for misuse detection. Moreover, machine learning and data mining can be used for anomaly detection and hybrid detection. Buczak (2016) mentions an example proposed by Lipmann, which utilizes keyword selection and artificial neural networks. The system was designed to determine the number of times each keyword occurred. The program achieved 80 percent detection of anomaly and one false alarm on a daily basis.

Conclusion

The advent of information technology in the various sectors of the economy has led to the development of data mining and machine learning. Companies and people exchange data daily, and the information needs to be secured to avoid criminal activities. Data mining and machine learning have proved to be useful techniques that have significant applications in various industries such as banking, retail, health, and security. Companies such as Facebook and Google utilize data mining and machine learning techniques to conduct crucial activities for customer satisfaction and retention. The advancements in development have led to the designing of new data mining and machine learning techniques to make data collection and interpretation easier to manipulate. Some of the recent methods mentioned for machine learning include kernel-based learning, hybrid active learning, distributed and parallel learning, and deep learning. Contemporary techniques in data mining include NoSQL, predictive analytics, and predictive analysis. Data mining and machine learning techniques will continue to be developed and will expand their applications to other sectors of the economy because there are other uses of both data mining and machine learning techniques that require further research and testing to certify their use.

References

Ali, A., Qadir, J., ur Rasool, R., Sathiaseelan, A., Zwitter, A., & Crowcroft, J. (2016). Big data for development: applications and techniques. Big Data Analytics, 1(1), 2.Battula, B. P., & Prasad, R. S. (2013). An overview of recent machine learning strategies in data mining. International Journal of Advanced Computer Science and Applications, 4(3), 50-54.

Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.

Gheware, S. D., Kejkar, A. S., & Tondare, S. M. (2014). Data mining: task, tools, techniques and applications. International Journal of Advanced Research in Computer and Communication Engineering, 3(10).

Kumar, D., & Bhardwaj, D. (2011). Rise of data mining: current and future application areas. IJCSI International Journal of Computer Science Issues, 8(5).

Silwattananusarn, T., & Tuamsuk, K. (2012). Data mining and its applications for knowledge management: a literature review from 2007 to 2012. International Journal of Data Mining & Knowledge Management Process (IJDKP) 2.5 13-24.

Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S. (2016). A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing, 2016(1), 67.