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Data Mining Assignment: Development of an Intelligent System


Task: Your report should be limited to approx. 1500 words (not including references). Use 1.5 spacing with a 12 point Times New Roman font. Though your paper will largely be based on the chosen article, you should use other sources to support your discussion or the chosen papers premises. Citation of sources is mandatory and must be in the IEEE style.

Your report or critique must include:

Title Page: The title of the assessment, the name of the paper you are reporting on and its authors, and your name and student ID.

Introduction: Identification of the paper you are critiquing/ reviewing, a statement of the purpose for your report and a brief outline of how you will discuss the selected article (one or two paragraphs).

Body of Report: Describe the intention and content of the article. If it is a research report, discuss the research method (survey, case study, observation, experiment, or other method) and findings. Comment on problems or issues highlighted by the authors. Report on results discussed and discuss the conclusions of the article and how they are relevant to the topics of this Unit of Study.

Conclusion: A summary of the points you have made in the body of the paper. The conclusion should not introduce any ‘new’ material that was not discussed in the body of the paper. (One or two paragraphs)

References: A list of sources used in your text. They should be listed alphabetically by (first) author’s family name. Follow the IEEE style.

The footer must include your name, student ID, and page number.

Note: reports submitted on papers which are not approved or not the approved paper registered for the student will not be graded and attract a zero (0) grade.


The data mining assignment serves to be a research report for addressing the Data Mining techniques that are used in intelligent systems. The paper would significantly introduce numerous pieces of literature and briefly analyse the case study for a detailed understanding. Appropriate research methodologies have been used for the completion of the entire study.

The report has been generated based on the case study as for the context. The case study in consideration is based on the development of an intelligent system (IS) with the help of precise data mining techniques. Thus, the paper would be closely associated with the learnings from the case study and a few other pieces of relevant pieces of literature.

Brief of Article
The article in consideration is briefly associated with the studies to implement appropriate data mining techniques in IS . With the help of the article, it has been observed that the IS in consideration has a specific use. The system is specifically designed to approve and simulate data of the logistics of the company. However, such data in consideration suffers crude handling and worse response time through normal use of databases. Thus, several data mining techniques and data warehousing and big data analysis would be an efficient place for increasing the productivity and the efficiency of the system.

It is not hard to note that every research has its own specific approach, which is decided by the researcher for the commencement of the research. Thus, accounting for the need for precise identification of the appropriate research design or rather the specific research methodology. Now the question arises for what exactly is the play of a research methodology. In simpler terms, it is one of the most crucial parts of the research which determines the probable steps or design to the entire research. The researchers have to make the hard call for the followed methodology . Research methodologies are mostly of two types, the quantitative and the qualitative. And according to the needs of the research, the methodology has been set. However, based on the requirements and the criteria of the research, it has been observed that the data collected would be mostly theoretical. Therefore, it is safe to presume that the research would be following a Qualitative methodology. However, the data from the case study accounts to a Quantitative approach which contradicts to the possibility of the mentioned. However, taking both into account the research methodology, which would be briefly followed for the commencement of the research would be the Mixed approach .

Literature review
This section of data mining assignment focuses on the numerous pieces of literature that focuses on similar scenarios. The possibility of data mining techniques, which is to be integrated into an intelligent system is limitless. It is essential to understand that the world is completely growing more into the digital aspects of the world . The digital world is slowly yet steadily taking over the physical aspects of the current world. Thus, data management is no different. The use of databases in place of filing systems had been long brought into practice. Yet, when tested with its limits, it was seen that due to technological limitations, further implementation was not completely what was desired for. It has been observed to be able to tackle the identified limitations. There had some form of advancements into the available technology. Thus, came in the concept of historical data and the validation and management of surge large data sets. At times such data sets are often called a data cube due to the three-dimensional logical design it carries. The ideas and the logical implementation are limitless, as the author suggests . However, it is essential to understand that as a new technology in practice, it has developed many tools for the entire system to be a smooth as possible.

How data mining assignment address the role of Decision Support System in Retails organisation?
Retail organizations have been the early adopters of Information Technology (IT). As there is a need to catch precise data and make it accessible inside the store as well as send it to stockroom, merchants and makers progressively to deal with the short timeframe of realistic usability of specific merchandise in fundamental food item division and expenses of stock, shifted composed retailers have received DSS instruments. VMI's-merchant oversaw stock frameworks, Scanner at the counters-purpose of offers frameworks, RFID-radio recurrence distinguishing proof, OLAP (online diagnostic preparing), inventory network the executive's frameworks, gauging frameworks, CRM customer relationship the board frameworks, ERP-undertaking asset execution framework and so on are the apparatuses utilized by composed retailers in created countries. Three capacities primarily portray DSS; exchanges, information and modelling the accentuation of each differ from association to association. DSS incorporates a wide assortment of frameworks, apparatuses and innovation that help essential leadership. Endeavour wide DSS are connected to large information distribution centres and serve a few chiefs in an organization though work area single client DSS are little frameworks dwelling on individual administrator's PC. Consequently, it is an intuitive modernized framework that accumulates and introduces information from a broad scope of sources, ordinarily for business purposes. The association needs to pool in both interior and outer knowledge, programming, client information, models and prepared individuals to acknowledge and utilize the frameworks for essential leadership, which will help construct maintainable upper hand.

  1. Customer’s Information Data Warehouse Design: It has been stated in this data mining assignment that the author suggests that currently, numerous companies and private companies are able to implement the idea of data mining. The concept is rather new; the possible limitation is still misty. However, it is essential to understand that data mining is mostly considered as the technique to be able to tap into a large amount of data. The data that is in consideration is the very aspect of a big data or even a data warehouse[1]. In the loop cycle of tasks and investigation, where examination is the arrangement of activities that you present to comprehend and refine your business is increasingly proficient or potentially compelling. An information distribution center gives the establishment to manage the customer information system in retail chain business data warehouse.
  2. Data warehouse: A Data Warehouse (frequently alluded to as an Enterprise Data Warehouse, or EDW) is a focal storehouse where a lot of business data, from different divisions and information sources, are incorporated.
  3. Association Rule Algorithm: Association rules algorithm is a system to reveal how things are related to one another. There are three traditional approaches to gauge affiliation.
  4. Component Design in Access Object: Managers and their help staffs in retail chain business need to think about what data and investigations are expected to help their administration and business exercises in order to achieve higher business growth. A few directors need both point by point exchange information and abridged exchange information.

Now, with the term data warehouse, it can be easily associated with certain logistics companies keeping track of the daily produces or even the products that have been shipped and been tracked during its transit. Such is the biggest example of Amazon. The e-commerce giant has been able to tap into the holy grail of data management with its cloud-based data warehouse. Most commonly known as the Amazon AWS, which is the Amazon cloud service. Tit keeps track of the company logistics and all the associated businesses it is currently holding. It is also important to understand that the company is able to rent their services to IT companies and any other company who wishes to take advantages of the data warehousing system and the associated data mining tools.

Case study
This section of the data mining assignment mainly pays the prime attention to the case study that has been taken into consideration for the research. It is important to note that the case study utilises the possible tools and techniques that would account for data mining in an intelligent system . The case study takes significant interest in analysing the data sources and the considered data warehouse for tapping into the possible use of the data mining tools. The technology of data warehousing is used to organise and sort the data systematically. The system under the context of the case study is the supply change management system of an organisation. Thus it is evident that such systems mostly make use of numerous data architecture to be able to reach an optimal form. The study does feature OLAP analysis on the considered IS for the analysis and understanding the possibility of the data mining tools in a supply chain management system . It has been observed that numerous angles of analysis have been performed through the system to be able to see the tangible outcome of the entire supply chain system. The study shows the possible changes and the possible implementations of data mining tools and data storage possibilities in any intelligent systems .

Data Mining
To be able to draw clear conclusions to the entire subject matter of data mining assignment, it is important to understand the possibilities and the concept of data mining clearly. As already stated, data mining is one of the essential aspects of data management in a large amount of data. However, data mining is simply looking for potential patterns in humongous data sets that would eventually help the data set to classify itself. It specifically is all about exploring the patterns or the relationships that a set of data may have with another . Thus data mining simply is the digging up of patterns in different data sets. However, it is important to note that there are numerous kinds of data mining to be talked about. Some of the essential ones are as follows:

  • Data warehousing
  • Data repositories
  • Object-oriented databases
  • Relational database or RDBMS
  • Web mining
  • Test database
  • Legacy databases
  • Homogeneous databases

It is thus crucial to be able to understand the flow of data mining. The stages and steps that account with the implementation of data mining are as follows:

  • Understanding the business
  • Understanding the data
  • Preparing the data
  • Modelling the data
  • Evaluating the data
  • Development of the data set

data mining assignment

(Figure: Stages of implementation of data mining)
(Source:, 2019)

Apart from the mentioned, there are a few essential aspects of data mining that needs to be addressed in this data mining assignment. The aspects that need to be addressed are the various facts of data mining techniques . There are seven possible techniques that would account for the possibility of the exercise of data mining. The techniques are as follows:

  • Clustering
  • Regression
  • Classification
  • Prediction
  • Sequencing
  • Outer detection
  • Rules associated

With the help of the stated techniques of data mining, the undiscovered patterns of data sets would be discovered. It can be observed that data mining is increasingly growing into popularity due to the right reasons. Thus, the needed education to be able to use the techniques efficiently and widely is of utmost importance.

data mining assignment

(Figure: Data mining Techniques)
(Source:, 2019)

Every project often carries a simple straight forward approach with a single aspect to be addressed ahead. Thus, the following are the key findings to the entire research.

  • OLAP analysis is performed for the full disclosure of the analysis and the feasibility check of the mining techniques.
  • The techniques used fie the mining methods does not need to be single.
  • The multi-angle analysis had been performed for the critical analysis of the entire data set.
  • The data sets need to be sorted strategically, thus allowing the least redundant data in the sets.
  • The implementation technique of data analysis is of numerous possibilities.

It is safe to conclude that the proposed technique illustrated in data mining assignment for the maintenance of data sets for a supply chain turns out to be feasible. Mainly due to the using of the technique of data warehousing. Though there are many options available for exercising the aspect of data mining, it turns out that warehousing is the easiest and most feasible to perform. The feasibility of the technique has been checked using the multi-angle analysis of the data set and maintain historical data for the organisation.

Reference List
[1]"Modeling of Intelligent Energy Distribution System", International Journal of Science and Research (IJSR), vol. 5, no. 1, pp. 2012-2018, 2016. Available: 10.21275/v5i1.20011601.

[2]P. Lugtig and N. Balluerka, "Methodology Turns 10", Methodology, vol. 11, no. 1, pp. 1-2, 2015. Available: 10.1027/1614-2241/a000092.

[3]E. Alexander, M. Eppler and S. Bresciani, data mining assignment "Visual Replay Methodology: A Mixed Methods Approach for Group Discussion Analysis", Journal of Mixed Methods Research, vol. 13, no. 1, pp. 33-51, 2016. Available: 10.1177/1558689816664479.

[4]"Data Mining from Heterogeneous Data Sources", International Journal of Science and Research (IJSR), vol. 6, no. 1, pp. 2076-2079, 2017. Available: 10.21275/art20164530.

[5]A. Aryal, Y. Liao, P. Nattuthurai and B. Li, "The emerging big data analytics and IoT in supply chain management: a systematic review", Supply Chain Management: An International Journal, 2018. Available: 10.1108/scm-03-2018-0149.

[6]S. Bankar, "Cloud Computing Using Amazon Web Services AWS", International Journal of Trend in Scientific Research and Development, vol. -2, no. -4, pp. 2156-2157, 2018. Available: 10.31142/ijtsrd14583.

[7]F. Alam, "Usage of data Data Mining Techniques for combating cyber security", International Journal Of Engineering And Computer Science, 2017. Available: 10.18535/ijecs/v6i1.31.

[8]Y. Yu, "Intrusion Detection of Masqueraders Based On Data Mining and Soft Computing Techniques", Journal of Software, vol. 10, no. 9, pp. 1111-1118, 2015. Available: 10.17706//jsw.10.9.1111-1118.

[9]Z. Xiaotao, "2018 International Conference on Virtual Reality and Intelligent Systems", Design of Intelligent Management Decision Support System for Retailing Chains, pp. 485-489, 2018. [Accessed 3 September 2019].


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