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Data Mining Techniques

Data Mining Techniques

A technological innovation converting data piles into knowledge that helps users utilize their choices better. Data mining thus looks out for hidden patterns within enormous data sets in order to understand and predict behavior while analyzing data adhering to distinguished dimensions. Identifying hidden patterns, data classification and summarizing hence play an intricate role in mining data. The prime elements included in the equation are extraction and transformation which help in managing a multi-dimensional database, business analytics, statistical analysis and data analysis & representation when it comes to generating revenue. Being a more than a valuable financial asset, data mining techniques need to be analyzed and implemented. Here are few measures as to how fine-tuning of data mining can be ensured:

  • Identifying data incompletion: Incomplete data presents off-mark results. Thus, it is always better to let the system find out incomplete data.

  • Feed dynamic data: Ensure live feed of real-time data onto the supervisor’s system. This monitoring technique adds extra advantage.

  • Analyzing database: Database holds information in structured formats, thus helping in building algorithms dedicated to data patterns.

  • Content analysis: Automatically finding patterns within enormous amounts of data becomes a helpful concept in text processing.

  • Complex data management: Large data stores need interactive support with query-based data mining for across differentiated data mining functions.

  • Scalability of algorithms: It is a prime factor to keep in mind when predicting the business relevance of enterprises that scalability of data mining algorithms reduces future costs.

  • Various tools for data mining: Ready-made tools are available in the present market for the data mining with highly-functioning packages supporting business specific analysis. Following are few trendsetting tools for data mining:

  1. Rapidminer: Being a ready-made and open-source software providing advanced analytics incorporating data mining functions and easy integration options with R-tool and WEKA.

  2. WEKA: WEKA being a JAVA based customization tool, it includes predictive analysis and visualization techniques

  3. R-Programming tool: This tool majorly allows data miners to develop scripts in C and FORTRAN similar to other programming platforms.

  4. Orange: An open source tool developed in Python dedicated to data and text analysis as well as machine learning through a visual programming interface.

  5. NLTK: A prominent language processing Python tool consisting of data scraping features as well as data mining techniques.

  6. KNIME: Knime being a data pre-processing GUI tool presenting data network nodes is popular among the data analyst fraternity building business intelligence.

We, at Hvantage Technologies, deliver the most competent data mining techniques and tools for the business in order to leverage existing data stores for the firm, providing a competitive edge in the market.


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