What could be the best topic to research in Data Mining?

Aug 08, 2019 · Popular Answers ( 2) A lot of data mining algorithms are lossy in terms of the meaningfulness of the result. I guess one could sometimes see data mining as a compression or a summarisation system. For (2), as already said by others - the implementation of data mining on cloud services is interesting, as is its application on healthcare.

What are the major problems facing in data mining? - Quora

From a purely technical perspective, the two problems I battle with when data mining are the time I spend doing it and the inability to measure the quality of the insights. The first one is related with the process. Data mining takes time. Each i...

Data Mining Practice Final Exam Solutions

Data Mining Practice Final Exam Solutions Note: This practice exam only includes questions for material after midterm—midterm exam ... did not come up on the homework because those problems were quite short. (If you do not know what the Apriori property is, do not panic. You will ultimately get the exact same answer but will just lose a few ...

Business problems for data mining - LinkedIn

Data mining techniques can be used in virtually all business applications, answering most types of business questions. With the availability of software today, all an individual needs is the ...

Business Problems Solved by Data Science | CoolaData Blog

Data mining is especially important for business managers because the data mined is usually marketing/business data. Data mining is also mainly used to analyze user behavior by searching for patterns and/or systematic relationships between variables, and then validating the findings by applying the detected patterns to new subsets of data, the ultimate goal here is prediction.

Problems Using Data Mining to Build Regression Models

Sep 21, 2016 · In this blog post, I'll illustrate the problems associated with using data mining to build a regression model in the context of a smaller-scale analysis. An Example of Using Data Mining to Build a Regression Model. My first order of business is to prove to .

12 common problems in Data Mining

Feb 03, 2015 · 12 common problems in Data Mining 1. Poor data quality such as noisy data, dirty data, missing values, inexact or incorrect values,... 2. Integrating conflicting or redundant data from different sources and forms: media files... 3. Proliferation of security and privacy concerns by ...

What is Data Mining? Solving Problems Through Patterns ...

Data mining and analysis is an attempt to make sense of it all and find the nuggets of information that can have a profound and useful impact on an organization. Data mining in the world of business Businesses can help maximize sales and revenue by better understanding how .

Advantages and Disadvantages of Data Mining

Data mining is an important part of knowledge discovery process that we can analyze an enormous set of data and get hidden and useful knowledge. Data mining is applied effectively not only in the business environment but also in other fields such as weather forecast, medicine, transportation, healthcare, insurance, government.etc. Data mining has a lot of advantages when using in a specific industry.

Major issues in data mining

Mining different kinds of knowledge databases: Data mining should cover a wide spectrum of data analysis and knowledge discovery tasks, including data characterization, discrimination, association, classification, clustering, tread and deviation analysis, and similarity analysis.

Top 5 Problems with Big Data (and how to solve them)

Problems with Big Data. Pioneers are finding ways to use Big Data insights to do such things as stopping credit card fraud, anticipating and intervening hardware failures, rerouting traffic to ...

What is data mining? - Definition from WhatIs

Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data .

Problem Definition - Data Mining Map

Problem Definition. Data science projects are often structured around the specific needs of an industry sector (as shown below) or even tailored and built for a single organization. A successful data science project starts from a well defined question or need.

Data Mining Survivor: Data_Mining - Business Problems

Data mining consists of ple data analysis and model building techniques that can be used to solve different types of problems in business. Although it is not the only solution to these problems, data mining is widely used because it suits best for the current data environments in enterprises.

The Problems with Data Mining - Schneier on Security

May 24, 2006 · The Problems with Data Mining. The sociologist Stanley Milgram made this clear in the 1960's when he took pairs of people unknown to each other, separated by a continent, and asked one of the pair to send a package to the other -- but only by passing the package to a person he knew, who could then send the package only to someone he knew, and so on.

10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH

One important problem is mining data streams in extremely large databases (e.g. 100TB). Satellite and computer network data can easily be of this scale. However, today's data mining technology is still too slow to handle data of this scale. In addition, data mining should be a continuous, online process, rather than an occasional one-shot process.

• Published in: International Journal of Information Technology and Decision Making · 2006Authors: Qiang Yang · Xindong WuAffiliation: Hong Kong University of Science and Technology · University of VermontAbout: Machine learning · Data mining · Knowledge extractionGET PRICE >>

Business problems for data mining - LinkedIn

- Business problems for data mining. Data mining techniques can be used in virtually all business applications, answering most types of business questions. With the availability of software today ...

Data Mining Problems - 1284 Words | Bartleby

Data mining & Evolutionary algorithms for Multi-objective Optimization problems: A study. Data mining is the process of extracting the knowledge from the huge database available. The ultimate aim of data mining involves prediction based on the knowledge gained. Data mining is known as Knowledge Discovery in Databases (KDD) which is different ways mainly prediction and description.

Top 5 Problems with Big Data (and how to solve them)

Problems with Big Data. Pioneers are finding ways to use Big Data insights to do such things as stopping credit card fraud, anticipating and intervening hardware failures, rerouting traffic to ...

What Is Data Mining? - Oracle Help Center

Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is .

5 real life applications of Data Mining and Business ...

The first example of Data Mining and Business Intelligence comes from service providers in the mobile phone and utilities industries. Mobile phone and utilities companies use Data Mining and Business Intelligence to predict 'churn', the terms they use for when a customer leaves their company to get their phone/gas/broadband from another provider.

Top 10 challenging problems in data mining | Data Mining ...

Mar 27, 2008 · Top 10 challenging problems in data mining. The "selective" process is the same as the one that has been used to identify the most important (according to answers of the survey) data mining problems. The paper by Yang and Wu has been published (in 2006) in the International Journal of Information Technology & Decision Making. The paper contains the following problems (in no specific .

Top 5 Data Mining Techniques

Sep 08, 2015 · Each of the following data mining techniques cater to a different business problem and provides a different insight. Knowing the type of business problem that you're trying to solve, will determine the type of data mining technique that will yield the best results.

The Real Problem with Data Mining | Brennan Center for Justice

The Real Problem with Data Mining. But the companies' success in detecting fraud is due to factors that don't exist in the counterterrorism context: the massive volume of transactions, the high rate of fraud, the existence of identifiable patterns (for instance, if a thief tests a .

Data Mining Problems in Retail – Highly Scalable Blog

Mar 10, 2015 · The role of data mining in the optimization problem (1) is crucial because econometric models are typically complex and have to be learned from data by means of regression and other data mining techniques.

Top 5 Data Mining Techniques

Sep 08, 2015 · Each of the following data mining techniques cater to a different business problem and provides a different insight. Knowing the type of business problem that you're trying to solve, will determine the type of data mining technique that will yield the best results.

5 real life applications of Data Mining and Business ...

The first example of Data Mining and Business Intelligence comes from service providers in the mobile phone and utilities industries. Mobile phone and utilities companies use Data Mining and Business Intelligence to predict 'churn', the terms they use for when a customer leaves their company to get their phone/gas/broadband from another provider.

10 techniques and practical examples of data mining in ...

It is a data mining technique that is useful in marketing to segment the database and, for example, send a promotion to the right target for that product or service (young people, mothers, pensioners, etc.).

Ethical Implications Of Data Mining! - Sollers

Mar 04, 2017 · Ethical Implications Of Data Mining! Data mining has been successfully used by retail, marketing, e-commerce, healthcare, and other business organizations. In the business sector like marketing, e-commerce, and retail data mining are used to analyze customer behavior to predict trends thereby enhancing a company's revenue or profits.

Using Data Mining to Select Regression Models Can Create ...

However, data mining problems can be more pronounced when you're using smaller data sets. That's the context that I'm writing about. Data mining is the process of exploring a data set and allowing the patterns in the sample to suggest the correct model rather than being guided by theory.