Posted in Community :
Statistical & Data Analytics |
Business intelligence and Data Warehousing |
Data Mining in Finance
Data Mining
Tags :
data mining, data mining techniques, data mining applications, analyst, data mining algorithms, data mining tools, data mining classification, data mining analysis, data warehousing mining, data mining concepts, data mining papers,
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IT Products, IT Services
Functional Area : Application Software |
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About "Data Mining" topic:
Share your thoughts on data mining, data mining techniques, data mining applications, data mining algorithms, data mining tools, data mining classification, data mining analysis, data warehousing mining, data mining concepts, data mining papers,
4 insight
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, 6 question
on topic: "Data Mining"
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Data mining
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Computational Statistics
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Sujeet Singh
| Argues in support of
"Data mining "
| 3 years ago
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To me, the concept of Data Mining is becoming increasingly popular as a business information management tool where it is expected to reveal knowledge structures that can guide decisions in conditions of limited certainty and recently, there has...
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Saurabh Ratnakar
| Argues in support of
" Computational Statistics"
| 3 years ago
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Computation statistics is the use of computers to derive solutions that can't be directly calculated. An applying of numerical methods to a statistics problem. This is what is typically implied when putting the word "computational" in front of a...
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Debate: "Data mining versus Computational Statistics" deleted from your view.
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I find that data mining and predictive analytics fall into the same category--they are the same basic technology but described from different perspectives. Predictive analytics is a term I see more in the CRM and database worlds. Perhaps some of this is due to the encroachment of BI into the data mining world, where queries and OLAP are sometimes called data mining (after all, you are drilling down into the data!). This would necessitate creating further distinctions in terminology. However, I don't see data mining losing hold on the style of predictive modeling that is largely empirical and data driven. Although we have heard many times that predictive analytics will optimize our marketing...
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Shashikant Brahmankar
| Commented
| 3 years ago
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Very good discussion point... often neglected by the majority... Some clarification needed here. 1. data mining and predictive analytics fall into the same category - No. Two different things. Crudely, data mining is equivalent to data...
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Arijay Chaudhry
| Commented
| 3 years ago
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For Predictive analytics, the best algorithms are those embedded into the IDASM. The systems undergoes over 85000 calculations per time-series and uses triangular and sinusoidal functions. The algorithm is considered to be 20% more accurate than...
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Insight: " Data Mining and Predictive Analytics" deleted from your view.
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Arijay Chaudhry
| Answered
| 3 years ago
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Advantage in Healthcare: Multidimensional Budgeting, Multidimensional Forecasting, Multidimensiona Reporting, Vendor Classification, Client Segmetnation, ABC Analysis, Promotion Optimization and Simulation, Business Reports and Variance Studies,...
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Ankur Agarwal
| Answered
| 4 years ago
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yes i would i like to elaborate on the question you hav askd!! There are numerous applications of data mining in healthcare and in its related disciplines of biotech, pharma and healthcare insurance. I see no disadvantages in the proper use of...
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Answer: "Can anyone share advantages or disadvantages of data mining in Healthcare Information Systems Management? I want to know..." deleted from your view.
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Please suggest either books or white papers and reports which are available for free on-line, and also from your experiences.
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Mousum Dutta
| Answered
| 3 years ago
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If you can tell something about domain of application then we can suggest you some better. If want for Market Research then " Applied Data Mining: Statistical Methods for Business and Industry (Statistics in Practice) " is a good book because it...
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Radhakrishna Marar
| Answered
| 4 years ago
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Even I was looking sor some good resources... Thanks Brajesh and Banerjee for providing information...
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Gangadhar GangaSagar
| Answered
| 4 years ago
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Assuming you do not have any background in statistics and have no idea about tools such as neural networks, clustering algorithms, then I would suggest following books - very reader friendly, and non-technical explanation of complex technical...
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Answer: "what is the best way to learn about data mining? Please suggest." deleted from your view.
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Sandeep Sharma
| Answered
| 4 years ago
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Data mining techniques are the result of a long process of research and product development. This evolution began when business data were first stored on computers, continued with improvements in data access, and more recently, generated...
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Answer: "How data mining supports business activities?" deleted from your view.
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Gangadhar GangaSagar
| Answered
| 4 years ago
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Data mining and day to day operations are not all that exclusive. A successful data mining project provides vital inputs as to how operations can be modified / optimized. Having said that, data mining projects can be used to develop models (based...
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Answer: "How does data mining help in controlling operational risk in banks?" deleted from your view.
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The future of data mining lies in predictive analytics. The technology innovations in data mining since 2000 have been truly Darwinian and show promise of consolidating and stabilizing around predictive analytics. Variations, novelties and new candidate features have been expressed in a proliferation of small start-ups that have been ruthlessly culled from the herd by a perfect storm of bad economic news. In addition to a perfect storm of tough economic times, now improving measurably, one reason data mining technology has not lived up to its promise is that 'data mining' is a vague and ambiguous term. It overlaps with data profiling, data warehousing and even such approaches to data analys...
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Mohan Nair
| Commented
| 3 years ago
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yes a good article indeed but then there is very good description of datamining overlapping with others in the process, and predictive analytics is going to be used in many ways like in banking and other core sectors as well.
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Insight: "Future of Data Mining - From obscurity to center stage " deleted from your view.
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Companies in the Insurance industry collected enormous amounts of data about their clients. This is invaluable information about customers behaviour, activities, and preferences. To extract information from the whole amount of raw data Insurance firms lost time and efforts, due to their protective regulations. Let us see how data mining can be used in insurance industry. 1. The first step for an analyst is to select databases (data warehouses) that can be used for knowledge discovery. 2. After selecting an appropriate database for data mining it is necessary to set up the data mining software. The most suitable data mining method for fraud detection in insurance is Classification. Data mini...
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Rajani Kanth
| Commented
| 4 years ago
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Data mining techniques can be used to analyse financial time series data, to find patterns, to detect anomalies and outliers, to recognize situations of chance and risk, to detect temporal changes in the correlation patterns and structures, to...
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Insight: "Step by step guide for using Data mining in insurance industry" deleted from your view.
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Sudhir Shirke
| Answered
| 4 years ago
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relational data mining (RDM) is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge....
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Answer: "Is relational data mining useful for financial data" deleted from your view.
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Avanish Ratna
| Answered
| 4 years ago
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Data mining in finance typically follows a set of general for any data mining task steps such as problem understanding, data collection and refining, building a model, model evaluation and deployment. Such techniques need to be integrated into...
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Answer: "What are the aspects of data mining methodology in finance ?" deleted from your view.
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