Application of Data Mining Techniques for Software Reuse Process

Prakash, B. V. Ajay; Ashoka, D. V.; Aradhya, V. N. Manjunath

doi:10.1016/j.protcy.2012.05.059

Cited by 18 publications

(19 citation statements)

References 18 publications

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“…Data Mining can be useful in all three phases of customer relationship cycle: Customer Acquisition, Increase Value of the customer and Customer Retention (Prakash et al, 2012;Ping and Liang, 2010). Financial organizations especially banking sector recruits Relationship Managers or team of executives to pay proper attention to their customers.…”

Section: Customer Relationship Managementmentioning

confidence: 99%

“…Due to the tight competition exists in the market (Sergio et al, 2011;Chen et al, 2009), customers will always with banks which provide better facility and more secured transaction option. Data Mining techniques (Prakash et al, 2012;Wikum et al, 2009) can be used to determine the list of customers as per the set of definitions (Sergio et al, 2011;Corne et al, 2012) and interest and the institution can offer better facilities to them (Abdullah and Titus, 2010) customers are varying from their approach in banking, like certain customers interested only electronic banking while others want banking through the counter. Classifying such customers can easily done using data mining techniques and provide better facilities.…”

Section: Customer Relationship Managementmentioning

confidence: 99%

“…It can be used to fasten the decision making and guide to forecast the effects of decisions (Prakash et al, 2012). Data Mining can be used to increase the response rate of marketing campaign.…”

Section: Customer Relationship Managementmentioning

confidence: 99%

“…Data analysis procedures can be classified as either exploratory or confirmatory, based on the availability of existing and appropriate models for the data source, but the main point of interest in both types of procedures (whether for hypothesis formation or decision-making) is the grouping, clustering or classification of measurements based on either (i) goodness-of-fit to a postulated model, or (ii) natural groupings (clustering) revealed through analysis (Shinde, 2012) • Fung et al (2010 Data Transformation. In this step, the selected data is transformed into the form appropriate for the input of data mining process (Han et al, 2011;Moin and Ahmed, 2012;Prakash et al, 2012) • Data Mining. This is the vital step on which effective algorithms (Bhambri, 2011;Hsu et al, 2012) and techniques applied to process the data into potentially useful patterns to achieve business objectives (Hammawa, 2011;Batini et al, 2009) • Evaluation.…”

Section: Introductionmentioning

confidence: 99%

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A Review on Data Mining in Banking Sector

Jayasree

Balan

2013

American Journal of Applied Sciences

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The banking industry has undergone various changes in the way they conduct the business and focus on modern technologies to compete the market. The banking industry has started realizing the importance of creating the knowledge base and its utilization for the benefits of the bank in the area of strategic planning to survive in the competitive market. In the modern era, the technologies are advanced and it facilitates to generate, capture and store data are increased enormously. Data is the most valuable asset, especially in financial industries. The value of this asset can be evaluated only if the organization can extract the valuable knowledge hidden in raw data. The increase in the huge volume of data as a part of day to day operations and through other internal and external sources, forces information technology industries to use technologies like data mining to transform knowledge from data. Data mining technology provides the facility to access the right information at the right time from huge volumes of raw data. Banking industries adopt the data mining technologies in various areas especially in customer segmentation and profitability, Predictions on Prices/Values of different investment products, money market business, fraudulent transaction detections, risk predictions, default prediction on pricing. It is a valuable tool which identifies potentially useful information from large amount of data, from which organization can gain a clear advantage over its competitors. This study shows the significance of data mining technologies and its advantages in the banking and financial sectors.

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Section: Customer Relationship Managementmentioning

confidence: 99%

Section: Customer Relationship Managementmentioning

confidence: 99%

Section: Customer Relationship Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review on Data Mining in Banking Sector

Jayasree

Balan

2013

American Journal of Applied Sciences

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“…Sonia et al [25] have proposed a framework for evaluating the reusability using metrics based approach. In [6] data mining technique is applied to extract useful knowledge from software repository using different software metrics. Washizaki [12] presented a metric suite for measuring reusability of software components.…”

Section: Related Workmentioning

confidence: 99%

Agglomerative Hierarchical Approach for Clustering Components of Similar Reusability

Jatain¹,

Nagpal²,

Gaur³

2013

IJCA

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This paper presents a clustering approach for grouping components of similar reusability using an already worked out fuzzy data set [2]. Research has shown that, component based systems development concept benefits the object oriented software development. A Component based system achieves flexibility by clearly separating the stable parts of systems from the specification of their composition. Many software systems contain many similar or even identical components and these components are developed from scratch over and over again which require extra effort. So to minimize the extra effort in developing these components, it is more beneficial to reuse the existing components. To reuse components effectively in Component Based Software Development, it is required to quantify the reusability of components. However it is difficult to use clustering approach to predict reusability. This paper discusses a technique to cluster components of similar reusability together for the purpose of minimizing the efforts of the developer using agglomerative hierarchical clustering. Components attribute affecting the reusability are classified into rules using fuzzy system and are then taken as the inputs to the proposed clustering model.

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Software reuse analytics using integrated random forest and gradient boosting machine learning algorithm

Sandhu

Batth

2020

Softw Pract Exp

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The term Cleaner Production (CP) for Production Companies is contemplated as influential to get sustainable production. CP mainly deals with three R's that is, reuse, reduce, and recycle. For software enterprise, the software reuse plays a pivotal role. Software reuse is a process of producing new products or software from the existing software by updating it. To extract useful information from the existing software data mining comes into light. The algorithms used for software reuse face issues related to maintenance cost, accuracy, and performance. Also, the currently used algorithm does not give accurate results on whether the component of software can be reused. Machine Learning gives the best results to predicate if the given software component is reusable or not. This paper introduces an integrated Random Forest and Gradient Boosting Machine Learning Algorithm (RFGBM) which test the reusability of the given software code considering the object‐oriented parameters such as cohesion, coupling, cyclomatic complexity, bugs, number of children, and depth inheritance tree. Further, the proposed algorithm is compared with J48, AdaBoostM1, LogitBoost, Part, One R, LMT, JRip, DecisionStump algorithms. Performance metrices like accuracy, error rate, Relative Absolute Error, and Mean Absolute Error are improved using RFGBM. This algorithm also utilizes data preprocessing with the help of an unsupervised filter to remove the missing value for efficiency improvement. Proposed algorithm outperforms existing in term of performance parameters.

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Application of Data Mining Techniques for Software Reuse Process

Cited by 18 publications

References 18 publications

A Review on Data Mining in Banking Sector

A Review on Data Mining in Banking Sector

Agglomerative Hierarchical Approach for Clustering Components of Similar Reusability

Software reuse analytics using integrated random forest and gradient boosting machine learning algorithm

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