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Let’s explore about Data Mining,
…“Data mining is a process of attaining useful data from large pool sets for analysing, discovering, exploring and building”. Data mining models can be used for various application scenarios such as: probability and risk analysis, forecasting, sequences finding, recommendations, and grouping.
Key stages involving in Data Mining:
- Data Pre-processing
- Feature Extraction
- Feature Selection
- Feature Weighting
- Generation of Prototype
- Instance Selection
- Data Classification
- Function of filter include swap attribute values filter, Numeric Transform filter, Delete filter, Select filter, Replace Missing values filter, Add filter, Merge Attribute filter, Nominal to binary filter.
- It is used specifically for Normalisation, transforming, Combing, resampling and transforming attributes.
- Logistic regression
- Multi-layer perceptron’s
- Gradient Boosted Decision Trees
- Naïve Bayes Algorithm
- Kernel Principal Components Analysis
- K-Means Algorithms
- First farthest
- Maximisation of expectation
- X-Means Algorithms
- Spatially Constrained Fuzzy-C-Means
Support for Plugins and Tools:
- Entropy Triangle Package
- Pentaho Data integration with Weka
- Pluggable evaluation metrics
Data mining Tools support:
- IBM SPSS Miner
- SAS EEnterprise Miner
- RapidMiner Rapid-I
A set of research issues and research projects are crafted and listed below especially for your reference.
- Network anomaly detection
- Categorisation and text mining
- Risk mining Acquisition
- Web mining applications
- Sentiment analysis
- With learning algorithm attacks can be predicted
- By using pattern recognition attack trends can be determined
- Advanced visualization for attack analysis
- Software risk management
- Big data applications
- Using data mining for network setting
- Creating unifying theory for data mining
- Levelling up data stream speed
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