LATEST DATA MINING PROJECTS
At TECHNOFIST we provide academic projects based on Data mining with latest IEEE papers implementation. Below mentioned are the list and abstracts on Data mining domain. For synopsis and IEEE papers please visit our head office and get registered.
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IEEE web mining/Data mining projects.
TECHNOFIST provides Data Mining based projects with latest IEEE concepts and training in Bangalore. We have 12 years experience in delivering Data Mining based projects with machine learning and artificial intelligence based applications with Python and JAVA coding. Below mentioned are few latest IEEE transactions on Data Mining.
Technofist is the best institute in Bangalore to carry out Data Mining based projects with machine learning and Artificial intelligence for final year academic project purpose. Latest Data Mining concepts for what is essential for final year engineering and Diploma students which includes Synopsis, Final report and PPT Presentations for each phase according to college format. Feel free to contact us for project ideas and abstracts.
Students of ECE, CSE , ISE , EEE and Telecommunication Engineering departments, willing to pursue final year project in stream of software projects using Python coding can download the project titles with abstracts below.
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IEEE WEB MINING/DATA MINING BASED PROJECTS
Data mining is the process of searching huge amount of data from different aspects and summarize it to useful information. Data mining is logical than physical subset. Our concerns usually implicate mining and text based classification on Data mining projects for Students.
The usages of variety of tools associated to data analysis for identifying relationships in data are the process for data mining. Our concern support data mining projects for IT and CSE students to carry out their academic research projects.
Hadoop framework includes following Modules:
- Hadoop MapReduce
- Hadoop Distributed File System (HDFS™)
Data mining is the process of searching huge amount of data from different aspects and summarize it to useful information. Data mining is logical than physical subset. Our concerns usually implicate mining and text based classification on Data mining projects for Students.
The usages of variety of tools associated to data analysis for identifying relationships in data are the process for data mining. Our concern support data mining projects for IT and CSE students to carry out their academic research projects.
Technics used for Data Mining
- Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
- Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
- Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
- Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".
- Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets.
- Summarization – providing a more compact representation of the data set, including visualization and report generation.
Data Mining Operations
- Link Analysis links between individuals rather than characterising whole
- Predictive Modelling (supervised learning) use observations to learn to predict
- Database Segmentation (unsupervised learning) partition data into similar groups.