Comparative Study of Data Mining Tools
Kalpana Rangra, Kishori Lal Bansal · Journal of Advanced Database Management & Systems · 2014
Data mining is a process which finds useful patterns from large amount of data for an appropriate application. It is a well-built technology with great potential to help companies whose focus is on important information in huge databases. It uses machine learning, statistical and visualization technique to discover and predict knowledge in a form which is easily understandable to the user. Classification is important technique of data mining which employs a set of pre-classified examples to develop a model that can classify the data and discover relationship between independent and dependent data. There are various algorithms in classification technique of data mining. Some of them are KNN, C4.5, ID3 and Rnd Tree. Different classification algorithms employ different theories to achieve the goal. There are many data mining tools available for extracting the hidden information from the structured and unstructured datasets. The tools used for comparison are WEKA, KEEL, KNIME, RAPIDMINER, ORANGE, and TANAGRA. Different data mining tools have got their own pros and cons. The main consequence of this fact is formulated by the ‘no-free lunch theorem’, which states that there is no universally best data mining tool. This triggers the need to select the appropriate data mining tool for a given application domain. So the objective is to study and classify the performance of various existing data mining tools. Cite this Article Nidhi Sharma, Bansal KL. Comparative Study of Data Mining Tools. Journal of Advanced Database Management & Systems. 2015; 2(2): 35–41p.