M.Tech Thesis Help in Data Mining | PhD Thesis Help in Data Mining
The field of data mining and knowledge discovery has been attracting a significant amount of research attention. An enormous amount of data has been generated every day. Data are being collected and accumulated at a dramatic pace due to the rapidly growing volumes of digital data. Data mining is the process of extracting useful information, patterns or inferences from large data repositories and it is used in various business domains. It involves finding valuable information and hidden inferences in large databases. With the help of data mining research Guidance, you can get all the latest topics related to readymade data mining thesis. Several domains where a large amount of data is stored in centralized or distributed databases and data mining thesis topics is found useful include the following:
- Financial investment: Stock indexes and prices, interest rates, credit card data, fraud detection, customer churn
- Health Care: Several diagnostic information stored by hospital management systems
- Scientific Domain: Astronomical observations, genomic data, biological data.
- Telecommunication network: Calling patterns and fault management systems.
- Manufacturing and Production: Process optimization and troubleshooting
- Worldwide Web
Data mining has been a potential tool to analyze data from distinctive points for retrieving useful information from chunks of raw data. Henceforth, it can help in predicting patterns or values, classification of data, categorization of data, finding correlations and patterns from the dataset. Moreover, the domain of data mining has been introducing numerous integration and advancements in the fields of Statistics, Databases, Machine Learning, Pattern Recognition, Artificial Intelligence, and Computational Capabilities.
The unexceptionally large volumes of data in human life have made the data mining an indispensable component. The emerging field of data mining aims at the extraction of new, valuable and non-trivial information from a large and abundant amount of data. Latest Thesis Trending domains to discover patterns or construct models are artificial intelligence, natural language processing, Machine learning, and statistics.
Data Mining Techniques
- Patterns of tracking. One of the most basic data mining techniques is to learn to recognize patterns in your data sets. Generally, this is a recognition of some aberration occurring at regular intervals in your results, or an ebb and flow over time of a certain variable. You might see, for example, that your sales of a particular product seem to spike just before the holidays or that warmer weather drives more people to your website.
- Classification is a more complicated technique of data mining that requires you to gather different attributes into discernible categories, which can then be used to draw more conclusions or serve some purpose. For example, if you analyze data on the financial histories of individual customers and the history of transactions,
- Association is similar to patterns of monitoring but is more common to variables correlated with dependency. In this case, you may check for specific events or attributes that are highly correlated with another event or attribute; for example, you will find that when your customers purchase a specific item, they frequently purchase a related item as well. Generally, this is what people are used to populating “people also bought” sections of online stores.
- Detection in outer space. Clearly knowing the underlying trend in many situations can not provide you with a clear understanding of your data set. You also need anomalies or outliers in your data to be identified. For instance, if your customers are almost entirely male, but there’s a big spike in female buyers during an odd week in July, you’re going to want to investigate the spike and see what caused it, so you can either duplicate it or better understand your audience.
- Clustering is very similar to sorting but consists of putting together chunks of data based on their similarities. For example, you may choose to bundle your audience’s different demographics into different packets based on how much disposable income they have or how much they choose to shop in your store.
- Regression, regression. Regression, commonly used as a method of forecasting and modeling, is used to determine a certain variable’s probability due to the presence of other variables. You might use it to project a certain price, for example, based on other factors such as supply, consumer demand, and competition. More precisely, the main focus of regression is to help you discover in a given data set the exact relationship between two (or more) variables.
- Prediction is one of the most valuable techniques of data mining as it is used to project the types of data that you will see in the future. In many situations, it is enough just to know and consider historical trends to make a somewhat accurate prediction of what will happen in the future. You could, for example, review the credit history of consumers and past purchases to predict if they will be a credit risk in the future.
What are the applications of data mining?
There is a wide range of data mining techniques (algorithms), which can be applied in all kinds of domains where data has to be analyzed. Some example of data mining applications are:
- fraud detection,
- stock market price prediction,
- analyzing the behavior of customers in terms of what they buy
In general data mining techniques are chosen based on:
The type of data to be analyzed,
the type of knowledge or patterns to be extracted from the data,
How the knowledge will be used.
M.Tech Thesis Help in Data Mining
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PhD Thesis Help in Data Mining
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