Review Paper on Data Mining Techniques and Applications

International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Volume-7, Issue-2, March 2019

5 Pages Posted: 2 Mar 2020

GVMGC Sonipat

Date Written: MARCH 31, 2019

Data mining is the process of extracting hidden and useful patterns and information from data. Data mining is a new technology that helps businesses to predict future trends and behaviors, allowing them to make proactive, knowledge driven decisions. The aim of this paper is to show the process of data mining and how it can help decision makers to make better decisions. Practically, data mining is really useful for any organization which has huge amount of data. Data mining help regular databases to perform faster. They also help to increase the profit, because of the correct decisions made with the help of data mining. This paper shows the various steps performed during the process of data mining and how it can be used by various industries to get better answers from huge amount of data.

Keywords: Data Mining, Regression, Time Series, Prediction, Association

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Writing a research paper (3) – the abstract

In this blog post, I will continue the discussion of  how to write research papers.   I will discuss the importance of  writing a good abstract for research papers,  common errors, and give some tips.

Why the abstract is important?

The abstract is often overlooked but it is one of the most important part of a  paper . The purpose of the abstract is to provide a short summary of a  paper .  A potential reader will often only look at the abstract and title to decide to read a  paper  or not.  A good abstract will increase the probability that a  paper  is read or cited, while a bad abstract will have the opposite effect.

The abstract is also very important because many papers are behind a paywall (a publisher will only provide the abstract and ask readers to pay to read the full  paper ).

What is the typical structure of an abstract?

The  structure of an abstract  is always more or less the same. Typically, it is a single paragraph,  containing five parts:

This type of structure gives a concise overview  of the content of the  paper .  The next paragraph gives an example of an abstract, which adopts this structure, from the  paper  describing the EFIM algorithm :

PART 1:  In recent years, high-utility itemset mining has emerged as an important data mining task.  PART 2 and 3:  However, it remains computationally expensive both in terms of runtime, and memory consumption. It is thus an important challenge to design more efficient algorithms for this task.  PART 4:  In this  paper , we address this issue by proposing a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discover high-utility itemsets. EFIM relies on two new upper-bounds named revised sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper-bounds in linear time and space.  (… )   PART 5:  An extensive experimental study on various datasets has shown that EFIM is in general two to three orders of magnitude faster than the state-of-art algorithms d2HUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+ on dense datasets and performs quite well on sparse datasets. Moreover, a key advantage of EFIM is its low memory consumption.

There is typically a maximum length restriction for an abstract. For example, some journals may require no more than 200 words. For a very short abstract, the PARTS 1,2,3 can be made very short or ommitted to focus on PART 4 and 5. For example:

PART 1,2,3:  High utility itemset mining has many applications but performance remains an important issue.  PART 4:  To address this problem, a novel algorithm named EFIM (EFficient high-utility Itemset Mining) is presented, which relies on two new upper-bounds to prune the search space, and a novel array-based utility counting technique.  PART 5:  Experiments have shown that EFIM has low memory consumption and is up to 50 times faster than state-of-art algorithms on dense datasets and performs quite well on sparse datasets.

For some other types of  paper  such as  survey papers  the structure is similar but some parts are omitted. Here is an example from a survey  paper  about  frequent itemset mining :

PART 1:  Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently co-occurring in databases. Due to its numerous applications in domains such as bioinformatics, text mining, product recommendation, e-learning, and web click stream analysis, itemset mining has become a popular  research  area.  PART 4:  This  paper  provides an up-to-date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high-utility itemset mining, rare itemset mining, fuzzy itemset mining and uncertain itemset mining. The  paper  also discusses  research  opportunities and the relationship to other popular pattern mining problems such as sequential pattern mining,  episode mining ,  subgraph mining  and association rule mining. Main open-source libraries of itemset mining implementations are also briefly presented.

Which verb tense should be used?

A good question is: Which verb tenses should be used in an abstract? Some general suggestions are:

Some common errors

I will now discuss six common errors found in abstracts:

Here are a few additional tips about  writing  an abstract:

That is all for this topic. I hope that you have enjoyed this blog post.  I will continue discussing  writing   research  papers in the next blog post. Looking forward to read your opinion and comments in the comment section below!

—- Philippe Fournier-Viger  is a professor of Computer Science and also the founder of the  open-source data mining software SPMF,  offering more than 145 data mining algorithms.

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Data Mining: Concepts and Methods Research Paper

Data mining can be defined as the process through which crucial data patterns can be identified from a large quantity of data. Data mining finds its applications in different industries due to a number of benefits that can be derived from its use. Various methods of data mining include predictive analysis, web mining, and clustering and association discovery (Han, Kamber and Pei, 2011).

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Each of these has a number of benefits to a business. In predictive analysis, analytical models are used to deliver solutions. Using this model, a business can uncover hidden data which can be utilized for the purposes of identifying trends and therefore, predicting the future.

This method requires a business to define the problem before data can be explored. There is also development of predictive models that must be tested. Finally, these models are applied in the population identification and in the prediction of behavior. The process followed helps a business to identify its current position in relations to the industry (Simsion and Witt, 2004).

From this, businesses can plan on how best they can improve their positions in relation to other companies in the industry. The trends obtained from analysis of the acquired data can be used for the purpose of planning which might further give a company an edge over its competitors.

In association discovery, the main aim is to discover correlation among different items that make up a shopping basket. The knowledge of these correlations is important in the development of effective marketing strategies. This is possible due to the insight gained on products that customers purchase together.

This method of data analysis can also help retailers in the design layout of their stores. In this layout, the retailer can conveniently place items that customer purchase together in order to make the shopping experience interesting to customers as well as increasing chances of high sales (Kantardzic, 2011). The method can also be used by a business to determine the products they should place on sale in order to promote the sale of items that go together with the first one.

Web mining is the process through which data present in the World Wide Web or data that has a relationship with a given website activity is made available for various business purposes.

This data can either be the contents of web pages found in various websites, profiles of website users, and information about the number of visitors in a given website among others. Web mining can be used by a business to personalize its products or services in order to meet specific needs of the customers. This is possible through tracking the movement of a given target customer on various web pages.

The method can also help a business improve on its marketing strategies through effective advertising. This can be achieved when used together with business intelligence. It also helps a business to identify the relevance of information present in its web sites and how it can improve this information with the view of increasing its visibility in the market.

Clustering involves grouping of data into specific classes based on specific characteristics (Han, Kamber and Pei, 2011). The process helps in the discovery of specific groups that the business should focus on. The method also helps a business to provide specific information that can be used to win over a given class of customers.

Data mining follows a sequence that ensures the data mined meets the requirements set down by the person mining it. Different algorithms handle the process of data mining differently based on the content of the data to be mined. Therefore, the reliability of the data obtained depends highly on the method used and the nature of data. Speed of data mining process is important as it has a role to play in the relevance of the data mined.

Therefore, a given algorithm should support speedy mining of data. The accuracy of data is also another factor that can be used to measure reliability of the mined data. For this reason an algorithm should be able to use specifications issued in the process of data mining. The two requirements for reliability are met by most algorithms which make them to be reliable for the purposes of data mining.

Various concerns arise over data mining and include invasion of privacy, ethics and legality. The issue of privacy arises when private information is obtained without the consent of its owners. Application of such information for business purposes can have detrimental effects to the business. Ethical issues arise when information mined is used by a business to take advantage of the owner of such information (Kantardzic, 2011).

There is also the question of legality of data mining without the consent of the person owning such information. To address the issues above, some businesses request permission from people before they can use information on them for various purposes which must be disclosed to the person.

Predictive analysis is used by businesses in market segmentation, analysis of the shopping basket and the planning of demand. Market segmentation enables a business to serve a given market better than if it had to serve a diverse market. In shopping basket analysis, a business can easily identify the products that are needed at specific times. The business can also determine demand and effectively plan how to meet it.

Han, J., Kamber, M. and Pei, J. (2011). Data Mining: Concepts and Techniques . Amsterdam: Elsevier

Kantardzic, M. (2011). Data Mining: Concepts, Models, Methods, and Algorithms. New York: John Wiley & Sons.

Simsion, G. C. and Witt, G. C. (2004). Data Modeling Essentials . Massachusetts: Morgan Kaufmann

Need a custom Research Paper sample written from scratch by professional specifically for you?

IvyPanda. (2019, August 20). Data Mining: Concepts and Methods.

IvyPanda. (2019, August 20). Data Mining: Concepts and Methods. Retrieved from

"Data Mining: Concepts and Methods." IvyPanda , 20 Aug. 2019,

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IvyPanda . "Data Mining: Concepts and Methods." August 20, 2019.

IvyPanda . 2019. "Data Mining: Concepts and Methods." August 20, 2019.

IvyPanda . (2019) 'Data Mining: Concepts and Methods'. 20 August.

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"Data Mining": Top 20 Topics to Research

If you want to conduct a research project on data mining and are looking for facts and topics, then you’ve come to the right place. The previous guide 10 facts on data mining for an academic research project must have given you a comprehensive outlook on data mining and you can get further help by reading this guide which has 20 interesting topics. In fact, not only does this guide provide 20 topics, but also an essay on one them to make it easier for you to start your research work today. If you want the specifics on how to approach this academic genre then feel free to go to our guide.

Data mining is a way to sample parts of a huge amount of data. These samples, further divided into variables, can then be used in mathematical calculations and algorithms. The algorithms make it possible to predict a pattern, which can then be utilized in thousands of applications. The purpose of data mining is to find patterns and this is the ethical line that needs to be kept in check. Here is a list of 20 topics which you can base your research project on:

Our objective is to help your train of thought get a direction so you can stop procrastinating and start working on your project. You can chose a topic from the above mentioned list or you can integrate two or more and make an even more detailed research project. There is a tsunami of information available on the internet about each and every one of the above mentioned topics so research won’t be an issue.

Try a quicker way

Sample Data Mining Project: Association Rule Learning in Data Mining

In data mining, association rule learning is an extremely vital tool through which two previously unrelated variables can be related in a significantly large data pool. Through this method, strong rules are successfully discovered in databases. Professor Rakesh Agrawal used the concept of strong rules to establish a different set of association rules that highlighted similarities between products even in huge amounts of transaction data in supermarkets.

If a log in the transaction data exists about a customer buying beer and potato chips, and if this is repeated by several other customers, we can safely establish the fact that the two products are connected. It is safe to assume that the next time a person buys beer, he or she will buy potato chips too. If a supermarket owner finds this out and puts the two products side by side, this assumption can turn into a fact, which will ultimately increase sales. This can also be used to design marketing campaigns. This mined data can help marketers put together two products in one picture to increase sales of both products.

Market basket analysis is an actual study which is being implemented not only in the supermarket industry but in web usage mining, continuous production, bioinformatics and intrusion detection too. Association rule learning is slightly different from sequence mining because it doesn’t take the order of items in a transaction under consideration.

Although used in many practical scenarios, association rule learning is not free of problems. One of the biggest issues with this method is that there is a significant chance of unusable or incorrect associations when an algorithm is going through massive numbers to locate items that seemed to be associated.

These incorrect associations occur by chance, as the associations between the items simply come forth due to unforeseen repetitions in the data. If the number of items is in the thousands, and the algorithm is trying to find an association between two items, then statistically speaking, there are thousands and thousands of possibilities. In this case there is the concept of statistically sound associations, which is designed to help reduce the amount of error in association though a more carefully coded probability algorithm.

There are some very famous algorithms designed over the years to create accurate association rules over the years. Although some famous algorithms exist such as Apriori, FP-Growth and Eclat, they can’t be expected to produce efficient results. In order to achieve specific and useful association results, one needs to go beyond the mining frequent item sets and create rules based on frequent item sets from a particular database.

References Shmueli, G., Bruce, P. C., & Patel, N. R. (2010). Data Mining For Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel® with XLMiner®, Second Edition. John Wiley & Sons. Steinbach, M., Tan, P., & Kumar, V. (2005). Data mining. Harlow: Addison-Wesley. Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques. Burlington, MA: Morgan Kaufmann. Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques concepts and techniques. San Francisco: Morgan Kaufmann In. Aggarwal, C. C. (2015). Data Mining: The textbook. Cham: Springer. Russell, M. A. (2013). Mining the Social Web: Data Mining from Facebook, Twitter, and LinkedIn, Google , GitHub, and More (2nd Edition). O’Reilly Media. Provost, F. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking.

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Research Paper

Data Mining, Research Paper Example

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Data Mining, Research Paper Example

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Data mining is a process attempting to make discoveries of patterns in large data sets. It does automate sensing of appropriate patterns available in a database. Data mining usually utilizes available methods at the intersection of database systems, machine learning, statistics, and artificial intelligence. Data mining process plays a significant role in extracting significant information from data set, then using its patterns transforms this information into a structure that can easily be understood for further use and functioning. Data management does not only involve raw analysis step, but it also several aspects. These aspects include online updating, complexity considerations, and model and interference considerations. It is important to note that data mining enhances post-processing of discovered structures, data processing, and data management processes (Pyle, 2003).

Data mining process involves automatic or semi-automatic analysis whereby large quantities of data are involved. These data usually extracted from patterns of data records and analysis such as association rule mining, anomaly detection and cluster analysis. All these analysis utilizes spatial indexes as an appropriate database technique. The process usually involves searching, cleaning, collecting, and analyzing data from different database sources with the sole purpose of evaluating them. The process can thus be said to be an automatic analysis of files found in online for the purposes of discovering patterns, which could have gone undiscovered and unexplored. Data mining involve several classes of tasks these include anomaly detection, association rule learning, classification, regression, clustering and summarization. Each of these classes is of significance in ensuring that the businesses or organization’s data and operations and handled appropriately.

Data mining algorithms comes after assembling of target data. Assembling is possible in situations where the target data is large and capable of containing the appropriate patterns and at the same time capable of being mined within the given time. Smart mart or data warehouse is usually the common source of data mining (Pyle, 2003). After data assembling has been done, the target data undergo cleaning where those observations, which contain noise, are normally removed and the ones with mining data set aside.

Data mining is technological advancement, which has resulted from the emergence of the IT industry and economic development. In this regards, data mining has now become a popular process. Several companies in the recent years are in need of solutions provided by data mining since it provides them with advantage over its competitors. With the aid of data mining, several companies have managed to gather data from various sources. This has increased benefits to the company in ensuring that efficiency is achieved. Business intelligence data mining have come up with the help of data mining, which involves gathering meaningful data from several sources especially online podiums. This is done with an intention of reaching at a sensitive business decisions (Pyle, 2003). This process usually includes economic trends, industry research, competitor and competition analysis, geographical information and market, and economic trends. With the help of data mining, various organizations and businesses have been able to manage their competitors.

Data mining helps companies and business entities in discovering information concerning their customers and the behavior of these customers towards products. In this regard, the businesses entities can then analyze, evaluate, store and synthesize crucial information from data related to the customers. Thus, data mining is a significant tool for organizations in enabling them makes improvements concerning their marketing strategies and provision of appropriate analysis concerning their customers.

The process has been of considerable help to organizations in providing solid customer focus this is because of its flexibility in its application and in foreseeing crucial data, which include customer-buying behavior, in addition to industry analysis.

Data mining process is a reliable process in undertaking business processes. It is one of the steps taking place between a business or a company and its customers. The influence on data mining on business is dependent on the processes of the business and not the process of data mining. Data mining results are usually distinct from those of other business processes, which are usually data-driven. Analysis of customer’s data using data mining shows that the results the user gets are the information known to them; and that they already existed in the database. Data mining has enabled businesses selling its products indifferent regions to translate easily the display of the information found to an appropriate understanding concerning various business processes.

The process is valid in that it extracts hidden information from the database, the user concerning its existence might not know some information. It has also aided in finding the relationship and connection between the customer’s behavior and different variables, which are normally non-intuitive. The advantage of data mining in this case is that it can utilize the output of its system after translating into solutions for business problems thus benefiting the business entity. Data mining has been a reliable process since its output has enabled the company to find the list of target customers and thus increasing their credit limit. The persons concern, in the process, has little task to accomplish since all the tasks has been accomplished by the data mining process, this has proved to be an effective approach and an efficient one thus affordable to any other business.

However, using results from data mining has proved to be a difficult means in using its results and getting the customers understand the process effectively and to take action in its operations. These processes can be of considerable benefit to customers in cases where the data mining system is made clear to the customers so that they can understand it qualitatively. It is imperative that failure to do so renders the process inappropriate (Kudyba & Hoptroff, 2001).

Assessing the reliability of this process can be achieved using several approaches. These methods include measuring statistical validity with the aim of determining where the problem are found. It involves separating data into training and testing its prediction accuracy and viewing the results with an effort of determining the meaningfulness of the discovered patterns. Utilizing all these methods leads to the effectiveness in using data mining process. Data mining can only trusted in situations where the company has effectively put in place the appropriate approaches in assessing the information found using this process. The process is, however, unreliable in cases where the extraction of information is extracted from the customer’s hidden behaviors and understanding these processes becomes complicated.

Data mining’s paramount concern is privacy. The technology of data mining is prone to abuse by different parties. For example, when one fills information in bank during loan processing, all the personal information is normally left in a database and are normally assessed by anyone (Soares & Ghani, 2010). This has led to cases of insecurities since thugs and robbers in tracing the person can use personal private information. Data mining usually make an assumption concerning the location of the information; they assume that the information in databases is held in one location within the organization. This, however, is not the case since information in the organizational database can fall in the hands of those who assess the database within and outside the organization, implying that private information are made public, and anyone can access in the internet thus privacy policy violated.

Privacy concern and legal issues in data mining are the leading source of conflict in business entities. In the recent past, government and corporate entities collect data and stored in data warehouses thus placing the privacy of consumers in a jeopardy state.

Consumers have, however, raised some privacy concern these include Secondary Use of the Personal Information, Handling Misinformation, Granulated Access to Personal Information and new privacy threats. The substantial privacy concern facing consumers is the use of private information, government and business entities normally access the information of the customers obtained from the organizational database and use it for other purposes mainly for their own benefits. This poses a problem in the side of consumers since their privacy is tampered with without relevant consultation. This concern is valid for consumers to raise since their privacy is interfered with and crucial information are left in the hands of strangers hence their security interfered (Shmueli et al, 2011).

Handling of misinformation by other parties who get access to customers’ private information in the company’s database, is also an ethical issue related to data mining. This information are usually prune to mishandling by the third party making the whole thing irrelevant. The consumers concern is valid since their personal information can be tampered in the hands of other internet users. New privacy threats as a privacy concern raised by the consumers, the threat is normally posed by Knowledge Discovery and Data Mining (KDDM), have lead to consumers information being interfered with. The threat normally includes deductive learning, data collection, and statistical analysis. This poses the problem to privacy of consumers since there is no guarantee of personal information being secure. The concern is valid since if left, personal information can get into the hands of individuals who are not trustworthy.

Kudyba, S. & Hoptroff, R. (2001). Data Mining and Business Intelligence: A Guide to Productivity. Idea Group Inc (IGI).

Pyle, D. (2003). Business Modeling and Data Mining. Morgan Kaufmann.

Shmueli, G., Patel, N., & Bruce, P. (2011). Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner. John Wiley & Sons.

Soares, C. & Ghani, R. (2010). Data Mining for Business Applications: Frontiers in Artificial Intelligence and Applications. IOS Press.

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