Best B9DA103 Big Data Mining Process Sample

Best B9DA103 Big Data Mining Process Sample

Introduction

Data mining has become one of the most important parts of today’s generation. In this report, the CRISP-DM (Cross-Industry Standard Process for Data Mining) is discussed.

The data mining process has revolutionized the business industries by giving access to a huge amount of data. In this article, the CRISP-DM model discusses the different phases of data mining projects such as data understanding, data preparation, modeling, evaluation, and development.

The data mining process helps in the extraction and analysis of large data which is an effective way for the business companies. Apart from these the data mining also has an application in business intelligence.

CRISP-DM MODEL OVERVIEW

CRISP-DM is an extensive data mining technique and also a process model that could help beginners to grow up as high-level experts (Azevedo, 2014).http://Best B9DA103 Big Data Mining Process Sample

This is mainly a basic outline for the process carried out in data mining. This model further divides the process of data mining projects into six stages:

business understanding, data understanding, data preparation, modeling, and deployment.

Different Phase of the CRISP-DM MODEL

The first phase is related to business understanding. This phase is the starting stage of the data mining process which particularly describes the initial project requirement with some trading context, thus developing the proficiency in data mining problems.

Similarly establishing initial planning for accomplishing the main objective of the project, to recognize which data should be evaluated and how useful and important it is for the practice of data mining the main objective of the business strategy.

The business understanding stage comes up with several key features that should be followed such as, determining business objectives which include the background of the project, the key purpose of business solution, and similarly developing the criteria for how to achieve progress in the following field.

Assessing the situation, second stage of analyses which includes outlining the assets required for personal as well technical use, the analysis should include the risks to be overcome, followed by solutions to those risks and planning for a profitable cost analysis of the project.

It objectifies the goals of data mining and creating a perfect project plan.

The second phase of elaborated on the initial stage of collection of data, thus the analyst looks forward to growing the intimacy for the quality of data problems, and discover more about the initial observation of such data or identifying various subdivisions assuming some hidden information present inside the data.

Data understanding process includes four steps to be followed those are: Collection of initial data, in this process the analyst achieves the necessary information which includes installing and combining the required data to avoid technical delays.

The second step involves describing the data in which the required data is verified by the analyst. Exploring the data, in this task, the analyst needs to be attentive about the serious visualization of fraud arrangements.

Verifying the quality of data at this stage the analyst verifies the data quality, also weather any information is missing in the given data, or the data provided is outdated.

Data preparation is the third phase of mining in this phase each stage is responsible for setting the data that would be placed in the mining tool from initial data.

This stage includes five steps selection of data which includes the data that is used for analysis and included in the process of data mining. Clean data, the data or the subdivision which are to be verified with integrated and advances techniques should be clean without any missing.

Construction of data, after cleaning of data the analyst develops a procedure of creating a fresh new record. Integrate data this stage involves the process of combing data from different records.

Formatting data at times the analyst needs to make some special changes in the data like changing some length of data given as input. Thus the data is ready to be used.

The fourth phase is modeling of data in which the required data is modified various techniques are used to generate a specific design for the model, building the model outline in details and last but not the least assessing the model easily

The fifth phase includes evaluation of the project before building the project, in this process the project is reviewed and the results are evaluated in every stage and then it is passed to the next stage of progress.

The sixth phase is the deployment of the project, at this stage, the projects need to be expanded such that importance of this project is known to every individual who might be using the process of data mining.

Analysis of Data Mining

Basic overview of R Programming

R programming is mainly based on statistical programming and is promoted by the R foundation (Goh Ming Hui, 2020).http://Best B9DA103 Big Data Mining Process Sample

This programming language is used for the analysis of data and statistical analysis this includes time series, learning algorithm, linear regression, statistical inference.

Data mining is a method of analyzing the arrangements of data using machine learning and managing various databases.

As the technology increases the use of machine learning and statistical learning use of sensual network and various decision trees which help the machine reveal the hidden arrangement of data.

Data understanding mainly includes the inspection of data with the help of demography and data visualization. For describing the data various graphs and charts are used. This stage instructs data quality.

The data preparation process involves including various subsections and selection of variables, including the missing values of the data that is analyzed, this is one of the most important and time-consuming stages of data mining.

This process rechecks the data before getting confirmed to the next stage.

Data modeling is a process of designing the data according to the required plan. The design of the model required is based on the outcome of the project objective.

This model can be developed with the help of a regression algorithm, and a machine learning algorithm.

Evaluation of the project is also a major factor in the process of data mining. In this process analyst evaluates the project thoroughly whether any corrections are required before the submission, at this stage, we can even go back to the previous stage if required.

The evaluation is carried out under the process of business success.

The Deployment stage is used for making new observations and knowledge to enhance the criteria or to make any changes to the organization. We can use the dummy model to create the final product and submit the conclusion based on the project objective.

Text mining is the process of mining the textual data such as data from blog posting, and twitter news feed. Text mining is the process of extracting high-quality explanations from textual data.

This process includes the classification of text, a gathering of text and text analyzing. This is known as text mining.

This is a basic analysis of data mining as per the project requirement.

Advanced Analytics Using Tableau and R

Here is the review of another related journal article in which the methodology of advanced analytics is discussed using R programming and tableau tool.

The data analysis is done with the help of R programming due to its vector foundation (Stirrup, 2020).http://Best B9DA103 Big Data Mining Process Sample

This helps in data tacking more easily. In R programming the vector is the core component. Essentially the vector is a data structure that contains an array. In the array, all the values are the same type such as string, or numbers.

The R programming is optimized to work with the vectors. With the help of R programming, data can be analyzed. This is possible due to its powerful data types.

The programming also contains a list which is a basic list and named list. The named list helps in mapping the data between R programming and tableau. In R programming the data structure is very important.

The data frames are the main data structure in R programming. The data frames also contain different types of data. The frame is very flexible in operation with the data which are structured.

In data mining, R programming can create data frames by accessing external data. User can create their data frames by assigning data to a variable.

In business intelligence industries the tableau tool plays a very important role. The tableau tool is an efficient data visualization tool that is used in business intelligence.

Tableau can help in data blending, real-time analysis, and collaboration of data. Tableau requires minimum technical or software skills. This tableau tool can define the business idea from a business perspective. This helps in identifying the possible scenario and in evaluation.

This also helps in project plan generation.

Big Data Mining

Big data mining is the collection of large data sets. It is referred to as large data collection or extraction techniques (Sowmya, 2017).http://Best B9DA103 Big Data Mining Process Sample

It is used for the large volume of data. This technique is used for the collection of large data by the organization for its benefits. The big data are used in data refining, data searching, and extraction of data and also used in comparing the algorithms.

Big data mining requires support from various fields such as underlying computing devices. In big data mining, the processors and memory of the computing devices are used for the operation of a large amount of data.

Apart from data collection, big data mining is also used in big data analytics and business intelligence. It is used in big data analytics and business intelligence to send briefed targeted and relevant information.

Big data mining is also used to send important information and patterns. The big data shows the relationship between data, systems, processes and many more. Apart from large data extraction, data also needs to be handled properly.

These big data are to be managed efficiently. The organization manages these structured and unstructured data properly. There is a separate interface that is used for the monitoring purpose of large data in an organization.

The database management is done regularly to have better results. Few other implementations help with the management of big data such as data analytics, big data reporting, and other similar solution.

To have efficient results organizations maintain highly effective design and implementation of data life cycle processes that help in gives a better output. Various techniques help to reduce the volume of the data and improve big data operations with faster access.

This data virtualization technique also helps in reducing the complexity of big data mining. This technique is also implemented for the usage of single data by various numbers of applications or users simultaneously.

The big data management also helps in mining and storing data from every source possible.

 

 

Tools and techniques of Big Data mining

As we know the data mining has a great application on business companies. Each data mining techniques is related to specific problems that are related to the business (Siddiqui, 2018).http://Best B9DA103 Big Data Mining Process Sample

Here are a few techniques that are used in the data mining process to have the required results.

  1. Classification analysis

This analysis is used to save important and applicable information about data and metadata. This helps in data classification of the various data type (Ratner, 2017).http://Best B9DA103 Big Data Mining Process Sample

The data is stored after the classification process in different classes. During the classification, the data analyst uses a data algorithm to decide the mode of classification.

  1. Association Rule Learning

In this process of data mining technique, the relation between different variables in large databases is identified. This technique helps to decode the hidden patterns in the data that can be used to identify variables within the data and the concurrence of different variables that appear very frequently in the dataset.

  1. Anomaly or outlier detection

During the data mining, the observation of data is required in which a similar type of data is collected. Anomalies refer to the dissimilarity, noise, deviation, in the collected data. These collected data are statistically compared to the rest of the data. This comparison helps in the detection of the dissimilarities in the data.

  1. Clustering Analysis

Cluster holds the data which are similar to each other. A cluster is a group of similar data. Cluster helps in analyzing similar data in such a way that the degree of association between two objects is highest if they belong to the same group and lowest otherwise. This helps in removing the irrelevant data from the datasets.

 

 

  1. Regression analysis

The regression analysis helps in analyzing the relationship between the variables. This process helps in understanding the importance and characteristic value of the dependent variable changes if any one of the independent variables is varied.

Various tools help n big data mining for data analysis. Some are discussed below.

  1. Apache Hadoop

This is software used for the grouped or clustered data system (Pawer, 2016).http://Best B9DA103 Big Data Mining Process Sample

This is also used in handling bigger data. Apache Hadoop uses MapReduce programming model to process large data sets. This tool is highly used in the research and development department. This tool also provides quick access to the data.

  1. Cloudera Distribution for Hadoop

Cloudera Distribution for Hadoop is an open-source tool that is used in comprehensive distribution. It is a tool that can be easily implemented. It has high security and governance. It has less complex administration.

  1. Cassandra

Cassandra is a tool that is used to manage the high volume of data that is spread across large commodity servers. This tool is used by high profile companies such as Facebook, General Electric, Honeywell, Yahoo, etc.

Cassandra has no single point of failure. This tool handles large data with more accuracy. This tool also provides log-structured storage for the data. Apart from these advantages, this tool requires some extra efforts in troubleshooting and maintenance.

Big Data Mining in Business Intelligence

Business intelligence is very important in today’s generation and it helps in providing historical, current and predictive views of business operations (Bayer et al., 2017).http://Best B9DA103 Big Data Mining Process Sample

The main function of business intelligence is to report, online analytical processing, analytics, data mining, process mining, complex event, processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.

The data mining tools are so important in getting business intelligence and it helps in various departments of business intelligence depending upon the requirement.

Data mining is a part of the business intelligence tool. The data mining helps in providing singularities, overviews, and full view of organization conditions. The data mining helps business intelligence to predict the action on dashboard visualization on market conditions.

The business intelligence is divided into three different levels such as reporting, integration, and analysis.

Application of data mining in business intelligence

In business intelligence, data mining helps in finding and analysis of cost and benefit ratio in the market (Peral et al., 2017).http://Best B9DA103 Big Data Mining Process Sample

Data mining helps in finding knowledge about the commercial sectors. It provides support to the research in the biological branch. It also helps in the prediction of telecommunication and helps in providing the recommendation in various fields with the help of big data analysis.

Data mining helps in target marketing which is very important in business. This helps in understanding the public demand. The data mining can help in predicting machine failure and malfunction.

Big data can help in data extraction and transformation that can benefit from business intelligence. These data are stored and managed in a multidimensional database system.

These data are analyzed by the application software. The data mining help in the restoration of the hidden data groups that is most relevant to the organization’s purpose.

This process is very important in practical analysis of any business industry. The data mining helps in securing important and valuable information such as consumer behavior patterns, frequency of shopping, customer personality profiles, and analysis of the current industry trends.

Data mining helps the business target their resources towards the vital areas of the operational process. Due to data mining, there is an increase in the processing capabilities of every company.

Business intelligence is termed as the collection of systems and the products that have been used in different business practices (Liang et al., 2018).http://Best B9DA103 Big Data Mining Process Sample

Big data helps provide information that is from outside the organization. This helps in accessing the data which are cannot be accessed by the company easily.

Big data are only used for large sets while business intelligence is used for all the data from the sales report of the organization. The software that is linked to business intelligence can process the standard data source. This software is not equipped to process big data.

The more advanced system is designed for the processing of the bigger data. The organization uses business intelligence to have optimized data records and to have tactical business decisions.

The business organization uses data mining to have sets of large raw data that are to be extracted from large unrecognizable patterns. The business intelligence uses complex raw data of an organization and transforms them into useful information as required by the business.

By using this useful information, the business will know what is working, what is not, what is the future, and how can you improve your business. Various important processes are involved in business intelligence.

These processes are related to the aggregation of complex data of an organization. These recorded data are analyzed. After the analyzing process, the data are presented in meaningful visualizations.

Business intelligence helps to make a useful decision for the company. Many tools in the market are used in business intelligence such as Micro Strategy, Tableau, Sisense, etc.

The company uses data mining to manage corrupt or irrelevant data. Multiple data are combined with meaningful information. Data mining also transforms the data into more relevant information that is useful in the presentation.

Conclusion

From the above discussion, we have seen the importance of data mining and its application in business intelligence. Each tool used in data mining is specified for specified problems only. These tools help in the extraction of large data that are used in business industries to analyze the different phenomenon.

Reference

Azevedo, A. and Santos, M. (n.d.) 2014. Integration of data mining in business intelligence systems.

Bayer, H., Aksogan, M., Celik, E., and Kondiloglu, A., 2017. Big data mining and business intelligence trends. Journal of Asian Business Strategy, 7(1), p.23.

Goh Ming Hui, E. (2020). Learn R for Applied Statistics. [online] Google Books. Available at:https://books.google.co.in/books?id=jN58DwAAQBAJ&printsec=frontcover&dq=The+C

Liang, T.P. and Liu, Y.H., 2018. Research landscape of business intelligence and big data analytics: A bibliometrics study. Expert Systems with Applications, 111, pp.2-10.

Pawar, A.M., 2016. Big data mining: challenges, technologies, tools and applications. Database Systems Journal, 7(2), pp.28-33.

Peral, J., Maté, A. and Marco, M., 2017. Application of data mining techniques to identify relevant key performance indicators. Computer Standards & Interfaces, 54, pp.76-85.

Ratner, B., 2017. Statistical and Machine-Learning Data Mining:: Techniques for Better Predictive Modeling and Analysis of Big Data. Chapman and Hall/CRC.

RISP-DM+Mode&hl=en&sa=X&ved=0ahUKEwid5-vYvPnnAhWi_XMBHRFoBnUQ6AEINjAC#v=onepage&q=The%20CRISP-DM%20Mode&f=false [Accessed 2 Mar. 2020].

Siddiqui, T. and Ahmad, A., 2018. Data mining tools and techniques for mining software repositories: a systematic review. In Big Data Analytics (pp. 717-726). Springer, Singapore.

Sowmya, R. and Suneetha, K.R., 2017, January. Data mining with big data. In 2017 11th International Conference on Intelligent Systems and Control (ISCO) (pp. 246-250). IEEE.

Stirrup, J. and Oliva Ramos, R. (2020). Advanced Analytics with R and Tableau. [online] Google Books. Available at: https://books.google.co.in/books?id=JJZGDwAAQBAJ&printsec=frontcover&dq=The+CRISP-DM+Mode&hl=en&sa=X&ved=0ahUKEwiIxtPejfvnAhVj4jgGHbP3DsAQ6AEIYDAH#v=onepage&q&f=false [Accessed 2 Mar. 2020].

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