Application Survey on Data Mining and Data Warehousing

Application Survey on Data Mining and Data Warehousing   Aishwarya.R Survey Report on Bank-Loan Risk Prediction Introduction Data Mining has been the most explored topic for the past decade and has given rise to several new enhancements and techniques in several industries. One such mind provoking arena of high interest is Credit Risk analysis or simply the Bank-loan risk prediction. It has been a pressing need for several banks these days to employ a Credit Risk Analysis simply to make sure that the money they invest to customers as a loan or any form is given to a legitimate customer who is capable of repaying and to avoid any other fraudulent scenarios. Several techniques in data mining have been explored to analyze the customers creditworthiness and a few will be analyzed and emphasized in the further sections. Discussion on Selected Papers In this Section, I have listed the journals, IEEE papers referenced for my study and analysis on Bank-loan risk prediction and categorized various factors for each in Table 1. Table 1. Sources used that focused on Bank-loan risk prediction using different data mining techniques References Objective Data Mining Techniques Employed Authors Number of Citations [1] SAS Enterprise Miner 5.3, Logistic Regression Model and Decision Tree employed in credit scoring models for assessing credit risk. Bee Wah Yap, Seng Huat Ong, Nor Huselina Mohamed Husain. 77 [2] Decision Tree model for credit assessments in a Bank. I Gusti Ngurah Narindra Mandala, Catharina Badra Nawangpalupia, FransiscusRian Praktiktoa. 15 [3] Predictive Modelling technique and NaÃÆ' ¯ve Bayes algorithm for loan risk assessment. Rob Gerritsen 34 [4] Multilayer Feed Forward Neural Network, Support Vector Machines, Genetic Programming, Logistic Regression, Group Method of Data Handling, Probabilistic Neural Network techniques for Financial Fraud assessment. P.Ravisankar, V.Ravi, G. Raghava Rao, I.Bose 147 Expert Systems with Applications: Using Data Mining to improve assessment of credit worthiness via credit scoring models Problem Description: Bee Wah Yap et al.[1] found a recreational club has been facing difficulties in identifying the defaulters who do not pay their monthly subscription fee causing a lot of chaos for the club to manage the funds effectively and divide the fund for any further activities or events in the club. The management decided to evaluate the credit worthiness of the club members by using the past members data as a data set and analyzed using three different data mining techniques in order to conclude the fittest of all[1]. Solution technology: Bee Wah Yap et al.[1] employed Credit scorecard model, logistic regression model and decision tree model using SAS ® Enterprise Miner, a diverse tool to employ several data mining techniques in order to improvise and identify the potential defaulters in the club. Solution Evaluation: Bee Wah Yap et al.[1] in the credit scorecard model, identified the various factors determining a defaulter based on their age, the number of dependents, the number of cars, district of address and most importantly the classification of defaulters and non-defaulters based on the payment status. They then obtained the Information Value as the summation of the probability of good attribute(applicable values from the old dataset taken for prediction) minus the probability of bad attribute(values from the old dataset that have no added value to be included in the prediction) and identified that values greater than 0.02 as admissible values of inclusion on the score card. They then identified the Stepwise selection method suitable of all the other Logistic Regression model and found a wide range of information and conclusions on the type of defaulters. Finally, they applied the Decision tree algorithm in order to classify an if-then rule for the large dataset into smaller segments and obtained the profile of defaulters. Based on the results he obtained from the above three techniques they had clearly identified that Decision Tree is by far a better approach for prediction although all three have no big difference and that Credit scoring model without adequate and proper data sets and old data could never perform well in prediction. Further Enhancements: The study has employed several techniques in order to justify a better model for prediction as a substitute for the Credit scoring model but has overlooked the fact that the data sets used throughout are from past customers which may or may not be legitimate way of prediction and definitely not a sensible way to conclude Decision Tree better over Credit scoring as neither of the arguments is valid and may vary when using a large amount of real-time data from the present to predict the future defaulters. Assessing Credit Risk: an Application of Data Mining in a Rural Bank Problem Description: I Gusti Ngurah Narindra Mandala et al.[2] felt that for rural banks to stay healthier, a certain benchmark has to be set on many factors out of which non-performing loan (NPL) factor played an important role. They identified that lower the NPL rate better the health of the rural bank. In order to employ this, they proposed that banks should approve only the right applicants and thereby increase the profit, credibility, and serve the improvements of their local community where such banks are most used. They were affirmative that banks with less than 5% of NPL are in better condition when compared to other with a greater value of NPL. Solution Technology: I Gusti Ngurah Narindra Mandala et al.[2] chose Decision Tree technique to be employed in a rural bank in Bali and scrutinized the various factors that are currently kept in consideration for lending loans to a customer. Solution Evaluation: I Gusti Ngurah Narindra Mandala et al.[2] found that the current NPL value of the rural bank of Bali is 11.99% very much higher than the expected value for a good performing bank. They made use of 84% of data from a sample data set of 1028 records for evaluation and determined approximately 13 parameters of consideration for evaluating the NPL customers. They developed a decision tree based on the existing parameters but reordered the determining factor as the collateral value and obtained an NPL of 3%, which by far is the most efficient a bank could perform. Further Enhancements: Although the above assessment and conclusion of a healthy bank seem appealing they could have employed a further emphasis on other factors that also contribute to a healthy bank / NPL and predicted the credibility further using various other Predictive and Descriptive modeling techniques which have better analysis and solution for the given scenario than what was obtained. Assessing Loan Risks: A Data Mining Case Study Problem Description: Rob Gerritsen [3] identified that if customers who could not pay their loans bank can be predicted before lending using data mining techniques then the information would be worthwhile. He found that USDAs Rural Housing Service has been lending money to people in the rural areas and USDA realized that the huge number of applicants who are being approved of the loan may or may not be capable of repaying the amount. Hence USDA decided to perform a data mining technique in order to gather the information and predict the vulnerabilities of the customers[3]. Solution Technology: Rob Gerritsen [3] decided to use Predictive Modeling Techniques along with the NaÃÆ' ¯ve Bayes algorithm to come up with a solution for the above problem. Solution Evaluation: Rob Gerritsen [3] was given a sample data of 12,000 based on the existing mortgages of single families and had to train the given data set using the model and then predict the future scenarios. So, he first classified the dataset and applied the NaÃÆ' ¯ve Bayes binning algorithm in order to divide the customer based on loan amounts that are to be paid by each. Initially, he found this ineffective as a huge amount of people fell into a single bin as the bin range values where continuous/uniform in distribution and hence difficult to identify precisely the original defaulters. He further organized the binning range distribution and made a decision tree from the results obtained to conclude the major factors of defaulters. Further Enhancements: Rob Gerritsen [3] himself has identified that the data set taken was too less to conclude the results and further, a wide range of dataset has to be taken along with further factors of consideration for USDA to obtain the verified solution for their problem. Decision Support System: Detection of financial statement fraud and feature selection using data mining techniques Problem Description: P. Ravisankar et al.[4] conducted a study on 202 Chinese companies using a variety of data mining techniques simply to conclude if the financial statements, income statements, cash flow, and various other factors if assimilated could give an better output from the companies and also decide if the loan has to be given to customers based on the results. Solution Technology: P. Ravisankar et al.[4] has employed a variety of data mining techniques namely Support Vector Machines (SVM), Group Method of Data Handling (GMDH), Genetic Programming (GP), Logistic Regression (LR), Multilayer Feed Forward Neural Network (MLFF) and Probabilistic Neural Network (PNN). He made use of a number of techniques for the same datasets in order to identify the best solution for the above problem. Solution Evaluation P. Ravisankar et al.[4] identified that among the 202 Chinese companies taken as a data set 101 were Fraudulent and the remaining were Non-Fraudulent. He then applied the Genetic Algorithm to find the fitness function, SVM to obtain the permissible support vectors, GMDH to classify and obtain a Feed Forward network model(Polynomial Model), PNN and with or without Feature selection in order to obtain the features of fraudulent companies. He has clearly observed that among the several techniques used the main factors that have to be considered is the amount of dataset that is to be used should concede with the capability of the technique and with less time consumption for training and obtaining results from the dataset. Further Enhancements I would abide with P. Ravisankar et al.[4] conclusion of classifying with an if-then rule on the dataset and to apply other hybrid data mining techniques inorder to further enhance the solutions. REFERENCES Yap, B. W., Ong, S. H., Husain, N. H. M. (2011). Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Systems with Applications, 38, 13274-13283. GustiNgurah Narindra Mandalaa, Catharina Badra Nawangpalupia*, FransiscusRian Praktiktoa Assessing Credit Risk: an Application of Data Mining in a Rural Bank / Procedia Economics and Finance 4 ( 2012 ) 406 412à ¢Ã‚ Ã‚  . R. Gerritsen, Assessing loan risks: a data mining case study, IEEE IT Professional (1999) 16-21. P. Ravisankar, V. Ravi, G. Rao, I. Bose, Detection of financial statement fraud and feature selection using data mining techniques, Decision Support Systems 50 (2) (2011) 491-500. Question and Answers Why DM and DW technologies are becoming important tools for todays business world? Todays business world is a competitive environment where right decisions needs to be taken at right time by knowing the answers for what has happened and by predicting what will happen in the future. Data warehousing helps us to identify answers for questions like what, which and how through aggregations. Data mining known as KDD helps us to predict what can happen in future. This is done by discovering and analyzing the hidden patterns. Both DM and DW results are processed from large set of data records from either same or different data sources. What are the main differences between data mining, traditional statistics data analysis, and information retrieval? Data Mining is a process of obtaining a derived / discovering new information based on the existing information by observing the data, identifying the patterns and obtaining meaningful analytics that can be used in business. A traditional statistics data analysis is method of testing a proposed phenomenon or hypothesis to validate and provide a statistically significant data for accepting the outcome. Information Retrieval in simple terms is the process of collecting/retrieving required data from an existing information available in any form. How is data warehouse model different from a relational database model? Why DW technology is more advanced in supporting business management? Relational Database Model: Used for Online Transaction Processing (OLTP) Data stored are generally a fact in a single operational database Tables are normalized SQL are used to query Data Warehouse Model: Used for Online Analytical Processing (OLAP) Data stored in DW are generally consolidated data(aggregation) from multiple databases or sources Tables are de-normalized OLAP tools are used to query The key difference between DW model and relational database model is that, DW is a layer on top of other databases whereas relations database is a database itself. DW technology is more advanced in supporting business management because it provides quick answer for question like WHAT, WHICH and HOW which helps the management to act accordingly on making decisions. i.e. they are very faster in generating reports for answering the management queries. What are the main difference between using OLAP on DW and using SQL on traditional database for supporting business decision making? The main difference is that complex questions which involves multiple aggregations can be answered in ad-hoc environments (i.e. data from different sources) easily in faster way using OLAP on DW

Reading for Pleasure Can Be Better Develped in Imagination and Language Sklls Than Watching Tv

Today, I will talk about an ancient city of China—Langzhong. My purpose in giving this presentation is to show you detailed geographic features of Langzhong, and recommend this famous place to you for travelling, so this presentation will have two parts, that is geography and tourism. Different from big modern cities, ancient cities can offer tourists an incredible variety of special sights and activities. This city is often referred to as a famous historical town. It’s one of the best preserved ancient towns in China. Langzhong city is a 2300-year-old city located in Nanchong, Sichuan Province.It is located in the north-east of the Sichuan basin and the middle reach of the Jialing River. The city is known as the wonderland of Sichuan. In terms of the geography of Langzhong, it is best to imagine the area as a main center surrounded by countless mountains and rivers with the northern part is higher than the southern part. Langzhong has plenty of natural resources such a s water resources, mineral resources and open space. Langzhong’s numerous rivers also ensure that our whole country has a major source of hydroelectric power. The government is also reclaiming these open spaces to improve agriculture.What’s more, Langzhong is rich in oil, gas, and gold. When people refer to Langzhong, they always talk about its long history. It was given the title of the best preserved ancient city around the world by the United Nations. As a travelling destination, Langzhong has something to offer almost every visitor. The first place that gives me a deep impression is Huaguang Tower. Huaguang Tower is also named Southern Tower or Zhenjiang Tower. It was first built in the Tang Dynasty, but it suffered several fires and was rebuilt repeatedly by past dynasties.The existing tower was renovated in the sixth year of the Tongzhi period of the Qing Dynasty. The base of the tower is 5 meters high and the total height is 25. 5 meters. With a three-layered ro of, the tower seems tall and straight as well as elegant and delicate. It has unique architectural features of the Tang Dynasty and the Qing Dynasty, winning the reputation of â€Å" the number one tower of Langyuan Garden†. In addition, few Langzhong sites are as impressive as the pavilion of Prince Teng, you can climb the pavilion to see the whole scenery of Langzhong City.The famous Chinese poet DuFu wrote an essay to praise this tower’s elegance and uniqueness highly–â€Å"The mountain is cloud-kissing, and people ascend it to enjoy a distant view. † If you want to know a lot about Chinese history and you are interested in the Han Dynasty, you can’t miss the chance to visit the Temple of Zhangfei. This temple has a history of 1700 years. It is well-known for its striking architecture, beautiful calligraphy, exhibition, and long history. Langzhong city is also famous for its examination hall.This hall was a special place for the imperial competi tive examination, which selected the most talented people and sent them to the emperor. Langzhong was called the town of number one scholars because it had so many geniuses who made great contribution to the entire world. What’s more, my favorites are the ancient streets and ancient houses. The preservation of the ancient city is a precious legacy for our whole world. Historians can learn a lot from this city. As for me, when I walked into the city, I simply wanted to spend a whole day strolling along the streets with my best friends.You can also take a boat to enjoy the scenery of Langzhong city along the Jialing River. This city is more peaceful than Lijing ancient city. Another thing the city has to offer is its excellent food. You can have a barbecue at night and drink specially-made plum wine or a beverage made of vinegar, both of which are delicious. The chafing dish has local features that are totally different from what I have ever eaten. That’s why I really wa nt to recommend it to you. There are some Langzhong’s tourist attractions, now, I want to talk about my trip to Langzhong. On that trip, I travelled with five friends.It was the first time that we took a trip without parents. We had to arrange everything in advance, but to be honest, it was hard to depend on ourselves completely. Nevertheless, I still enjoyed the trip and had great fun with my friends. We could visit any place we are interested in without a touring party. I think you can imagine how free we felt. In fact, I was not very close with two of those guys. Although we were classmates in middle school, we lost contact with each other after we graduated. After those five days, we had already become close friends and talked all the time.Yes, this trip was an valuable experience for me. Because of it, I understand it is difficult but important to be independent, I find it is so good to have such kind friends and I have fallen in love with this ancient city. It was just a five day long trip, but it becomes an precious part of my life. I know I will cherish it forever. That’s my speech. I hope everyone has learned something about Langzhong ancient city from it. If you want to experience the atmosphere of ancient China with your best friends, Langzhong is the place to do it! Thank you for your time.

Relative Clause ESL Lesson for Specific Purposes

Relative clauses are used to describe the noun naming the process or position when discussing tasks that need to be completed, or explaining how certain things work. The ability to use relative clauses easily is important to all English learners, but perhaps even more important to those wanting to use English in their workplaces. For example, salespeople need to explain and define anything relating to the use of the goods or services being sold. The Instaplug is a device that allows you to use any type of outlet throughout the world.Our Ontime Service is a type of consulting which allows you to access consulting services 24/7.The Sansolat Tile is a roofing tile which reflects sunlight in order to keep air conditioning costs down. Another example would be of the use of relative clauses to describe people at work: Youll need to speak to Mr. Adams who is ​in  charge of vacation and sick leave requests.Jack Wanders is the union organizer who represents this region.We need consultants who can travel anywhere on 24-hour notice. This lesson plan focuses on helping students learn to use relative clauses to discuss important issues at work such as who works with them, various types of work and workplaces, as well as describing goods or services manufactured or provided by their employer. Aim Building confidence in using relative clauses to describe goods, services, personnel and other related workplace situations. Activity Sentence matching, followed by guided writing exercise Level Intermediate to advanced English for Specific Purposes learners Outline Introduce students to the topic of using relative clauses by asking a few questions such as:How would you describe a blue collar worker?Whats full time work?Who is a consultant?What is a computer lab?These questions should elicit a number of responses, hopefully a few with competent use of relative clauses. Make sure to rephrase student answers throughout using relative clauses to help inductively introduce the idea of relative clause use. For example:Oh, full time work is a type of work which takes place for at least 40 hours a week.Good, yes, a consultant is someone who provides services and advice to a company on a contractual basis. etc.Once you have completed this warm-up, write four sentences on the board. Use one sentence with a relative clause referring a person with that and one with who. The other two sentences should refer to things; one beginning with that and the other with which. Ask students to point out these differences and explain why which or who is used, as well a s what. As far as is possible, try to coax the students into inductively stating the rules for relative clause use.Ask students to complete the sentences in the exercise below by choosing the two halves that go together and connecting each with a relative pronoun (who, which or that).Check answers as a class.Ask students to imagine ten items or people that are important to them in day-to-day work. Students should first write a list of the ten items / people. On another sheet of paper, ask students to write explanatory sentences using relative clauses.Have students exchange their ten item lists with a partner. Students should then practice explaining these items to each other using relative clauses. Students should not simply read what they have written, but try to use their examples as a starting point. Encourage students to ask probing questions based on the information they hear.Circulate about the room and help students. Once the exercise is finished, go over common mistakes youv e heard while listening to student pair work. Matching Halves Match the first half of the sentence in A with the appropriate phrase in B to complete the definition. Use an appropriate relative pronoun (who, which or that) to connect the two sentences. A A supervisor is a personI have difficulties with bossesOffice Suite is a group of programsSuccess on the road can be assisted by the cloudThe human resources director is the liaisonUse the ratchet as a toolInternal office communications are handled by our company forumYoull find that Anita is a personI couldnt get my work done without DarenTaplist is an app B you can contact to resolve contract issues.can tighten a wide variety of nuts and bolts.provides a friendly place to post questions, make comments and discuss issues.I use to keep track of all my mileage, meals and other work expenses.allows me to access documents and other data from a wide range of devices.do not take my point of view into consideration.is willing to help with any problem you may have.assists me with day-to-day tasks.directs employees working in a team.is used for word processing, creating spreadsheets and presentations.

The Concentration Of Behavior Analysis - 1875 Words

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