Data management techniques
Artificial Neural Networks -
It is the resemblance of biological neural network structures, which was trained through Non-Predictive models.
Decision Trees -
Dataset classification rules are designed with a treelike structure. These designs represent a set of decisions which we call it as decision trees.
Genetic Algorithms -
The process of genetic combination, mutation, and natural selection are optimized by the design based concept of evolution.
Nearest neighbour -
The combination of the data set is classified in accordance with the classes of records.
Rule Induction -
The extraction of useful if-then rules from data based on statistical significance.
Data Visualization -
For multidimensional rational data, data visualization is applied because it is geometric based, pixel oriented, icon based, hierarchical technique.
Pre-processing of Data
Data pre-processing deals with the quality of data. Quality in the sense, accuracy, completeness, timeliness, interpretability, believability and consistency. The pre-processing of data includes -
Data Cleaning is the process in which, removal of the outlier is done at the same time Cleaning up of the data set is carried out, to free from noise and make sure that all values are recorded correctly without inconsistent data set.
To consolidate and manage multiple data sources we have to go for the Data Integration method. It also merges the data from many sources into coherent data.
Selecting the relevant data according to the analysis
It is the process of reducing the number of random variables present in the data set, in order to make the data with small volumes which includes dimensionality reduction, numerosity reduction, and data compression.
The Data is transformed from one format into another format in order to perform mining operation. After normalization of data, the accuracy, efficiency, and range of the data are improved.
Data Mining is the concept of extracting the data patterns from the large data set.
Data Mining Software’s and Application
In today’s dynamic business world, so many researches are conducted to review the data mining survey. Carrot2, ELKI, GATE, KNIME, NLTK, UIMA, OpenNN, R, Angoss Knowledge STUDIO, LIONsolver, SAS Enterprise Miner, Qlucore, Oracle Data Mining, Microsoft Analysis Services, IBM SPSS Modeler are the most common Data Mining Softwares and applications used in the commercial, marketing, genetics as well as in the cybernetics, in order to Promote and Develop the business.
Data Mining used in the following Industries
- Business Applications
- Science and Engineering
- Marketing and Sales
- Human Resource Departments
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