Top PDF Data Mining Techniques for Banking Applications

Data Mining Techniques for Banking Applications

Data Mining Techniques for Banking Applications

Abstract: Financial segment is the core division to decide the country’s gross domestic product ( GDP). The considerable and constant development of any country is based on the financial strength of the country. Last few decades observered the increase of financial reforms, liberalization and globalization of Indian financial system coupled with rapid revolution in information technology (IT).The paper presents the advantages of applying data warehousing and data mining (DWDM) techniques in customer relationship management (CRM) of the financial divisions like banking. It is a procedure of analyzing the data from various perceptions and summarizing it into precious information. Data mining ( DM) aids the banks to look for unknown pattern in a group and determine unknown relationship in the data. These methods facilitate useful data analysis for the banking division to avoid customer harassment. And also fraud is an important problem in banking domain. Identifying and preventing fraud is hard, because fraudsters develop new techniques all the time, and the techniques grow more and more difficult to avoid easy finding.
Show more

6 Read more

Applications of Data Mining Techniques in Telecom
Churn Prediction

Applications of Data Mining Techniques in Telecom Churn Prediction

In a business environment, the term, customer attrition simply refers to the customers leaving one business service to another. Customer churn or subscriber churn is also similar to attrition, which is the process of customers switching from one service provider to another anonymously. From a machine learning perspective, churn prediction is a supervised (i.e. labeled) problem defined as follows: Given a predefined forecast horizon, the goal is to predict the future churners over that horizon, given the data associated with each subscriber in the network [7]. The churn prediction problem represented here involves 3 phases, namely, i) training phase, ii) test phase, iii) prediction phase. The input for this problem includes the data on past calls for each mobile subscriber, together with all personal and business information that is maintained by the service provider. In addition, for the training phase, labels are provided in the form of a list of churners. After the model is trained with highest accuracy, the model must be able to predict the list of churners from the real dataset which does not include any churn label. In the perspective of knowledge discovery process, this problem is categorized as predictive mining or predictive modeling. Churn Prediction is a phenomenon which is used to identify the possible churners in advance before they leave the network. This helps the CRM department to prevent subscribers who are likely to churn in future by taking the required retention policies to attract the likely churners and to retain them. Thereby, the potential loss of the company could be avoided. This study utilizes data mining techniques to identify the churners.
Show more

5 Read more

Big Data Applications

Big Data Applications

Big Data plays a vital role in various fields. Big Data nowadays being a challenging research area has attracted a lot of attention from academia, industry as well as government. Big Data is collection of large and complex data sets that are difficult to process using traditional data processing tools. We are creating trillions of data all over the world. This data is coming from social networking sites, scientific experiments, mobile conversations, sensor networks and various other sources. These day’s Big data is most preferably used in enterprises, organizations, companies and business etc…So Big data has number of applications in various fields such as banking, agriculture, chemistry, data mining, cloudcomputing, finance, marketing, stocks, BDA, health care etc…This paper explanation some of applications of Big Data. Various papers based on big data have been studied so far and I explore the usage of Big Data in several areas like data mining, cloud computing, Banking, Marketing, Health care, Finance, Enterprise applications.
Show more

5 Read more

Data Mining Techniques and its Applications : An Approach to Discover Knowledge in Data

Data Mining Techniques and its Applications : An Approach to Discover Knowledge in Data

Classification is the most commonly applied data mining technique, which employs a set of pre- classified examples to develop a model that can classify the population of records at large. Fraud detection and credit risk applications are particularly well suited to this type of analysis. This approach frequently employs decision tree or neural network- based classification algorithms. The data classification process involves learning and classification. In Learning the training data are analyzed by classification algorithm. In classification test data are used to estimate the accuracy of the classification rules. If the accuracy is acceptable the rules can be applied to the new data tuple. For a fraud detection application, this would include complete records of both fraudulent and valid activities determined on a record-by-record basis. The classifier-training algorithm uses these pre-classified examples to determine the set of parameters required for proper discrimination. The algorithm then encodes these parameters into a model called a classifier.
Show more

5 Read more

To Study the Various Techniques or its Applications in Data Mining

To Study the Various Techniques or its Applications in Data Mining

Comprehensive data warehouses that integrate operational data with customer, supplier, and market information have resulted in an explosion of information. Competition requires timely and sophisticated analysis on an integrated view of the data. However, there is a growing gap between more powerful storage and retrieval systems and the users‘ ability to effectively analyze and act on the information they contain. Both relational and OLAP technologies have tremendous capabilities for navigating massive data warehouses, but brute force navigation of data is not enough. A new technological leap is needed to structure and prioritize information for specific end-user problems. The data mining tools can make this leap. Quantifiable business benefits have been proven through the integration of data mining with current information systems, and new products are on the horizon that will bring this integration to an even wider audience of users.
Show more

14 Read more

Applications and Trends in Data Mining

Applications and Trends in Data Mining

The advent of Computing Technology has significantly influenced our lives and two major impacts of this effect are Business Data Processing and Scientific Computing. During the early years of the development of computer techniques for business, computer professionals were concerned with designing files to store the data so that information could be efficiently retrieved. There were restrictions on storage size for storing data and on the speed of accessing the data. Needless to say, the activity was restricted to a very few, highly qualified professional. Then came an era when Database management System simplified the task. The responsibility of intricate tasks, such as declarative aspects of the programs was passed on to the database administrator and the user could pose his query in simpler languages such as query languages. Thus almost any business-small, medium or large scale began using computers for day-to-day activities.
Show more

7 Read more

Applications and Techniques of Educational Data Mining in the Field of Higher Education (A review on Data Mining and Educational Data Mining in Education Sector)

Applications and Techniques of Educational Data Mining in the Field of Higher Education (A review on Data Mining and Educational Data Mining in Education Sector)

Data mining is one of the effective techniques of extracting meaning data a from large database. a Educational data mining is one of the sub branch of data mining where all the data related to result, performance, behavioral analysis of students is extracted. This paper gives a brief overview of Educational Data mining, its objectives and various techniques for higher education.

6 Read more

Parametric Comparisons of Classification Techniques in Data Mining Applications

Parametric Comparisons of Classification Techniques in Data Mining Applications

This paper describes goals of Data Mining (DM), phases of Data Mining, Data Mining Methods (Tasks) as well as the Issues of Data Mining and Data Mining Applications. In this paper, we did the comparative study of different Data Mining Classification techniques with their algorithms on various data sets using Weka tool. We also did the comparative analysis on the basis of accuracy percentage. We also analyzed the advantages of algorithms that applied to various data sets. It is difficult to say that which Classification technique of Data Mining is best because each technique has its own advantages and limitations and it also depend upon the purpose for which data is to be mined.
Show more

7 Read more

Applications of Operations Research and Data Mining Techniques in the Healthcare Sector

Applications of Operations Research and Data Mining Techniques in the Healthcare Sector

Data mining and operations research applications can be greatly beneficial but at the same time suffer from limitations as well. Since, raw inputs for data mining often exist in different setting and systems, such as administration, clinic, laboratories and more, they have to be collected and integrated, thus making accessibility of the data the biggest limitation. This is costly and time consuming as well. This limitation can be overcome through data warehouses, as then by ‘Intermountain Healthcare’. They built a warehouse from five sources – a clinical data repository, acute care case mix system, laboratory information system, ambulatory case mix system and health plans database. Another organization is ‘ Maccabi Healthcare Services ’ who used existing databases to guide subsequent data mining.
Show more

13 Read more

Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks

Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks

A major challenge facing healthcare organizations (hospitals, medical centers) is the provision of quality services at affordable costs. Quality service implies diagnosing patients correctly and administering treatments that are effective. Poor clinical decisions can lead to disastrous consequences which are therefore unacceptable. Hospitals must also minimize the cost of clinical tests. They can achieve these results by employing appropriate computer-based information and/or decision support systems. Health care data is massive. It includes patient centric data, resource management data and transformed data. Health care organizations must have ability to analyze data. Treatment records of millions of patients can be stored and computerized and data mining techniques may help in answering several important and critical questions related to health care.
Show more

6 Read more

Research Trends in Privacy Preserving in Association Rule Mining (PPARM) On Horizontally Partitioned Database

Research Trends in Privacy Preserving in Association Rule Mining (PPARM) On Horizontally Partitioned Database

Abstract - The advancement in data mining techniques plays an important role in many applications. In context of privacy and security issues, the problems caused by association rule mining technique are investigated by many research scholars. It is proved that the misuse of this technique may reveal the database owner’s sensitive and private information to others. Many researchers have put their effort to preserve privacy in Association Rule Mining. In this paper, we have presented the survey about the techniques and algorithms used for preserving privacy in association rule mining with horizontally partitioned database.
Show more

6 Read more

Find like-minded user using Big Data Mining Technique: A Case Study on Twitter

Find like-minded user using Big Data Mining Technique: A Case Study on Twitter

Social media using data mining techniques: a survey of big data. They state that it's very difficult to modify and structure the data that the internet user generating daily on liking, poking, tweeting, chatting on social media via traditional databases. This paper deals with all these 5Vs of big data [3]. Improve user RTSE experience on the web through fast retrieval of social media content: They proposed the framework to enhance the system efficiency. Web 2.0 applications producing real-time content continuously i.e Twitter and Facebook, at a very rapid pace [4].
Show more

6 Read more

Pattern Mining Techniques of Data Mining

Pattern Mining Techniques of Data Mining

Abstract— Frequent patterns are patterns that appear in a data set frequently. This method searches for recurring relationship in a given data set. Several techniques have been proposed to improve the performance of frequent pattern mining algorithms. This paper presents revision of different frequent mining techniques including Apriori based algorithms, association rule based, Cp tree, and FP growth. A brief description of each technique has been provided. In the earlier, different frequent pattern mining techniques are compared based on various parameters of importance. We have studied different techniques for frequent pattern mining proposed by different researchers and scientists. Each technique has its own merits and demerits. Performance of particular technique depends on input data and available resources. These techniques are found in many applications such as market basket approach, including applications in marketing and e-commerce, classification, clustering, web mining, bio-informatics and finance.
Show more

7 Read more

DATA MINING AS BEDROCK FOR BANKING

DATA MINING AS BEDROCK FOR BANKING

240 | P a g e 1) Classification Methods : In this approach, risk levels are organized into two categories based on past default history. For example, customers with past default history can be classified into "risky" group, whereas the rest are placed as "safe" group. Using this categorization information as target of prediction, Decision Tree and Rule Induction techniques can be used to build models that can predict default risk levels of new loan applications.

9 Read more

ROLE OF KNOWLEDGE MANAGEMENT TECHNIQUES IN DATA MINING FOR LARGE SCALE DATA IN BANKS

ROLE OF KNOWLEDGE MANAGEMENT TECHNIQUES IN DATA MINING FOR LARGE SCALE DATA IN BANKS

The banking sector is always aimed at improving customer satisfaction, which will result in increased revenues. The process of creating, archiving and dispersing knowledge becomes essential and banks assign specialized personnel to monitor and manage these critical processes. The most common fields of knowledge management applications in a bank are risk management, marketing management, customer relationship management and performance measurement, particularly for the benefit of its stakeholders. In general, investments in major banks in knowledge management systems, such as the Decision Support System, Data Warehouses and Fata mining are growing rapidly. The bank managers of these days use the computerized support system as personal support in the decision- making process. The knowledge management system in banks guarantees
Show more

9 Read more

Internet of Things and data mining: From applications to techniques and systems

Internet of Things and data mining: From applications to techniques and systems

levels of granularity. Thus, such methods are naturally cloud-based. However, in applica- tions with sufficient storage capacity at the edge, batch methods are still valid solutions, including healthcare applications for home monitoring of an elderly or a patient with a chronic condition. Vital signs monitored continuously can be stored locally, and suitable data mining algorithms can be applied accordingly. A large number of methods lie under this category, including classification and regression methods for predictive data mining, and clustering and link/affinity analysis methods for descriptive data mining. More recently, deep learning methods have been used to mine IoT sourced data (e.g., (Kim & Kim, 2017) and (Lane, Bhattacharya, Georgiev, Forlivesi, & Kawsar, 2016)). The authors in (Alam, Mehmood, Katib, & Albeshri, 2016) analysed the use of 8 data mining techniques on IoT data sets. These techniques include Support Vector Machine, K-Nearest Neighbours, Linear Discriminant Analysis, Naive Bayes, C4.5 decision trees, C5.0 decision trees, Artificial Neu- ral Networks, and Deep Neural Networks. The experimental work in this paper concluded that decision trees are favourable in terms of accuracy and computational overhead.
Show more

50 Read more

An Overview of Data Mining Techniques and Applications and its Future Scope

An Overview of Data Mining Techniques and Applications and its Future Scope

Data mining is a process which finds useful patterns from large amount of data. The paper discusses few of the data mining techniques, algorithms and some of the organizations which have adapted data mining technology to improve their businesses and found excellent results. The greater part of data mining methods can manage distinctive information sorts.Data mining may be defined as the science of extracting useful information from databases. It also called knowledge discovery. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future.
Show more

9 Read more

Application of data mining techniques for outlier mining in medical databases

Application of data mining techniques for outlier mining in medical databases

Outlier detection is one of the most important tasks in data analysis. An outlier is an extreme observation. Typically points farther than, say, three or four standard deviations from the mean are considered as “outliers”. In regression however, the situation is somewhat more complex in the sense that some outlying points will have more influence on the regression than others. Outlier detection has been suggested for numerous applications, such as credit and fraud detection, clinical trials, voting, irregularity analysis, network intrusion, severe weather prediction, geographic information system, and other data mining tasks (Barnett and Lewis, 1995; Fawcett and Provost, 1997; Hawkins, 1980; and Penny and Jolliffe, 2001). Outliers in a data may be due to recording errors or system noise of various kinds, and as such needs to be cleaned with regard to extract, transform, clean and load phase (ETCL) of the data mining/KDD process. On the other hand an outlier or small group of outliers may be quite error-free recordings that represent the most important part of a data that deserve further careful inspection, e.g., an outlier might represent an unusually high response to a particular advertising campaign, or an unusually effective dose-response combination in a drug therapy (Ben-Gal I, 2005). Either way, it is quite important in data mining to detect outliers in large amounts of highly
Show more

6 Read more

Data Mining for Banking and Finance

Data Mining for Banking and Finance

considerable developments in information technology have led to huge demand for continuous analysis of resulting data. Data mining can contribute to solving business problems in banking and finance by finding patterns, causalities, and correlations in business information and market prices that are not immediately apparent to managers because the volume data is too large or is generated too quickly to screen by experts. The managers of the banks may go a step further to find the sequences, episodes and periodicity of the transaction behaviour of their customers which may help them in actually better segmenting, targeting, acquiring, retaining and maintaining a profitable customer base. Business Intelligence and data mining techniques can also help them in identifying various classes of customers and come up with a class based product and/or pricing approach that may garner better revenue management as well.
Show more

8 Read more

Data Mining: Techniques, Tools and Applications

Data Mining: Techniques, Tools and Applications

Developing KDD- Applications Supported by Index- Structures) ELKI is open source data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. ELKI offers many data index structures such as the R*-tree that can provide major performance gain and in order to achieve high performance and scalability. The approach used is the independence of file parsers or database connections, data types, distances, distance functions, and data mining algorithms.
Show more

11 Read more

Show all 10000 documents...