RFData Mining Tools: OmniViz and Aureka
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Data mining refers to the extraction of relevant data from the large pool of data available in databases, data warehouses, the World Wide Web, and other repositories. It extracts useful patterns, trends, and insights from large datasets. It involves various tools and techniques that help researchers and analysts uncover hidden information within the data.
Data mining is a key part of KDD (Knowledge Discovery in Databases). The entire KDD process is divided into the following steps or sub-processes:
- Data selection
- Data Cleaning
- Data transformation
- Pattern searching (i.e., Data mining, Finding presentation, finding interpretation, finding evaluation).
Data Mining Techniques
The data mining techniques are listed as follows:
- Link Analysis: Association rules, sequential patterns, time sequences
- Predictive Modelling: Tree induction, neural nets, regression
- Database Segmentation: Clustering, k-means
- Deviation Detection: Visualisation, statistics
- Text mining: Extracts valuable information from textual data including techniques such as topic modeling, sentiment analysis, and named entity recognition.
- Time Series Analysis: Analyzes data points collected over time to identify patterns, trends, and seasonality.
There are other techniques for data mining, which include machine learning, database systems, rough sets, neural networks, etc.
Data Mining Tools
The table below (Table 1) mentions data mining tools with descriptions. The tools are divided into two groups.
The first group consists of Aureka and STN AnaVist, which does not require any learning by the user. These first-group-based tools are easy to use and offer basic analysis with minimal effort.
The second group consists of OmniViz and TDA VantagePoint. These tools can be used for any kind of data. Some learning is required to use these tools. Both tools come with default values and provide filters/wizards to import the data.
| Data Mining Tools | Description |
|---|---|
| Aureka | Developed by Thomson Reuters, the tool uses data retrieved from the MicroPatent database. |
| STN Anavist | Developed by the American Chemical Society, the tool uses data retrieved from four STN patent databases. |
| OmniViz | Developed by BioWisdom, the tool is designed to analyze biological data. It can be used for other technologies also. It provides many different visualization techniques. It is flexible, efficient, and interactive in nature. It is a great tool for users having knowledge of data mining methods and algorithms. Any format of data can be treated with the OmniViz data mining tool. The relevant filtered data can be exported to Microsoft Excel. |
| Thomson Data Analyzer (VantagePoint) | Developed by Thomson Reuters. It uses VantagePoint software for analysis. VantagePoint is developed by Search Technology. It also analyzes data in all formats. It offers three types of pre-defined reports. The reports include Company Report, Company Comparison Report, and Technology Report. |
Data Mining Companies or Vendors
As data mining provides very valuable information from large sets of data, it is used across many technologies and domains. It includes financial data analysis, retail industry, telecommunication industry, bio-logical data analysis, other scientific applications, etc.
There are data mining tools specifically developed to address these vivid market requirements. The following table (Table 2) mentions some of them with the corresponding company.
| Data Mining Tools | Company |
|---|---|
| ADAPA | Zementis Inc. |
| Coheris SPAD | Coheris |
| Data Applied By | Data Applied, it is a web service for data analysis. |
| GhostMiner | FQS Poland, Fujitsu |
| SPM (Salford Predictive Modeling suite) | Salford systems |
| IBM SPSS Modeler | IBM |
| SAS enterprise miner | SAS institute |
| D2K | University of Illinois |
| Revolution R Enterprise | Revolution Analytics |
| Data Detective | Sentient |
Conclusion
These tools and techniques developed by various companies are used to extract insights and make informed decisions based on data-driven analysis in many use cases.
