Current Developments in Data Mining, Machine Learning Techniques and the Use of Both Technologies in Various Fields

Introduction

I thank you for spending a lot of times in this papers by including all this information, which is good for the readers to follow it easily, but my instructor's comments are that "In the current format, your essay is far more informative than it is argumentative. I'm not really sure what opinion you are presenting here." I suggest to making it argumentative, the below points may help if you can clearly present it throughout the paper, they could be two paragraphs if possible.

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Both data mining and machine learning are significant in knowledge management.

It is essential for people to be concerned with this topic because in the contemporary society individuals are interacting with so many types of data such as photos, video files, and many others.

Sending and receiving data over the cloud requires through maintenance and management techniques to ensure a successful operation. Therefore, it is crucial for individuals to learn how companies manage to control such data without crashing.

The research proposal will argue that the current techniques in data mining and machine learning have opened these two technologies to an array of different applications in various sectors.

The research paper must present a clear central argument can easily be identified in the first paragraph.

Importance of learning maintenance and management techniques to ensure successful operations through sending and receiving data over the cloud

The advent of information technology has influenced various activities in 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. Big Businesses such as Facebook and Google control the flow of a massive piece 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 allows computational systems to learn from saved data how to implement 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, engineers have improved and applied more novel and efficient data mining and machine learning techniques.

The Recent Machine Learning and Data Mining Techniques

Basic and the 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 measures gathered from various 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 commentary 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 drawing samples from the origin 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 skirts the method that assembles data within one 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 similar trait space and distribution. By data originating from various 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 the specific function (Qiu et al., 2016).

Deep learning is the most recent technique used in machine learning. Deep learning utilizes both managed and unsupervised methodologies within difficult architectures to learn hierarchical representations automatically. Difficult structures can 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 random data such as social media data, word files, 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 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 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 is used in the fight against terrorism. After 9/11 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 the anomaly and one false alarm on a daily basis.

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. The development of Data mining and machine learning have shown 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 continue to be regularly developed; therefore, it will expand their applications to other sectors of the economy. This is because there are other uses of both data mining and machine learning techniques that require further research and testing to certify their use.

In today's business world, technology advancements are the order of the day as people experience a great shift in the Big Data era. Data mining (DM) in this case, which is regarded as a tool in information technology its strength is immense. Different machine learning, mathematical modeling, human intervention are a number of things this era tends to be more of (Buczak, 2016). However, in the previous years, companies have been able to apply data mining as a strategy for their business in order to capture more customers and ensure that they maintain those that are present. Nevertheless, using current forms such as mathematical programming, neural netw...