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Typical Data Mining task: Looking for a needle in a haystack!

Guido, nice post.
It is a useful formula, but I quit using it some years ago, because the probabilities calculated by the model are only the probabilities of the training data. To know the real value of the model, I always check it against a second dataset containing new data that are more recent than the training data. For it is obvious: you want to use the model to predict the future, so you should check your model against data from the future. The real world validation is the only thing you can trust in data mining, otherwise you never know whether the model is time-robust, overfitted etc.
About simply duplicating records : plainly put : it ads no information whatsoever, it only consumes diskspace. If you want to duplicate records you should do it in an intelligent way. This is nicely described by Dorian Pyle in his book “”Data Preparation for Data Mining”, which I still find the best data mining book I ever laid eyes on.

We listed the applications of data mining thesis are:

In this process we split data base into multiple groups to identify the difference among groups. We perform clustering process based on some attribute values. For clustering process data mining ensure various type of algorithm such as expectation maximization algorithm, K- means, single leakage clustering, fuzzy c means and DBSCAN algorithm.

How to choose a good thesis topic in Data Mining? - …

Thanks Brian, and the answer is yes – since your algorithm is bound to your subset. And if you linearly change your subset before using it, you can change it back linearly afterwards, it is technically not interfering with your algorithm in any way. So it does not depend on any data mining technique. Though chances are high that your result will be different (and better, hopefully). If you are into this you might want to read by Gary King and Langche Zeng.

Big data Thesis has gained wide importance by data mining research community because of its velocity, volume and variability. Many challenges are faced in data mining of large amount data from data users. Programming models, valuable algorithm and application framework are provider in data mining researcher. In all engineering and science field data mining applications are used. Our concern has supported more than 380 big data Thesis. To get back relevant and accurate feedback services some enhancement are being supported.

Overrepresentation - "SAS"-Oversampling - Data Mining

Second Tier: Knowledge of various big data uses and its semantic are consisted in second tier. To break technical barriers and for data mining process it is very useful.

Data Mining and OLAP (On-Line Analytical Processing): OLAP referred as detective process which produces hypothetical patterns and relationship. It analyzes data mining tools and finds risk factor in that tool.

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Data mining | Custom PHD Thesis

Data Mining and Ware Housing: Data ware house gather multiple sources and load data into database which placed in an enterprise area. Data mining also extracts data from data warehouse. Major benefits of adopting data ware house is mining when data is already present then is no need of data analysis process. Both OLAP and data mining are differs based on Hypothetical pattern evaluation.

Text & Data Mining of PhD theses - has anyone tried it?

Out of pure curiosity: what is the difference between oversampling and undersampling? I am asking because I found some others articles about data mining and it seems that some of them would rather call what you described here as undersampling []. Basically dropping non-target variables would make a whole data set smaller thus undersampling. On the other hand, after oversampling (in example that you provided) would look more like this:

PhD Research topic in data mining - PHD Projects

Hello. Does this formula work for ANY data mining algorithm that produces a probability? So, SVM to decision trees to logistic regression to neural nets?

Data Mining Thesis | Data Mining Thesis Topics

In cases where the target variable appears in a fraction of less then 10%, it is common to stratify the occurrence of the target variable. That should improve the result of your Data Mining challange. The term “oversampling” is used by SAS in their Enterprise Miner Software, to higher the relative occurence of the target variable without using copies – but by reducing the occurence of the non-target variable. Be advised that “oversampling” is also called to duplicate the content – you should check that out at . We will stick to the quite simple view of SAS.

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Phd In Computer Science Thesis In Data Mining

Final tier: To handle the complexities occurring in big data volume by providing a complex and dynamic data the final tier consist of data mining algorithm.

PhD Topics in Data Mining - Thesis and Code

To improve your Data Mining result when only having a small amount of target variables, it is useful to oversample the target variable. It is shown here how this works – and how to undo it when dealing with the result.

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