Data mining is the field of computer science that involves analyzing and exploring large sets of data for recovering some pattern or extracting some valuable information that might be hidden in the data that is almost useless if not properly managed. It helps the companies to focus on important data set instead of dealing with heaps of data.
Data mining is also helpful in predicting future behaviors and trends based on which many major companies take their decisions and choose their strategies. Today all the leading companies like Amazon and Google are vigorously working to be the market leader in this technology because according to researchers it is the next big thing and most exciting technology.
Data mining is closely associated with many other computer science fields including machine learning, artificial intelligence, database etc. Data mining involves using these technologies along with specific algorithms for extracting valuable information and presenting it in a structured and understandable manner.
Lots of tools are already available for data mining. Though data mining requires lot of experience and expertise but some of the tools have been developed for beginners who have not much experience in this field. Below are some of the tools that are widely used because they are comparatively easy to operate and can be choose based on specific requirements of the user.
Python tools for data mining:
Data Mining is a very complex technology, so we can employ already available tools on the internet for mining data. Python is the most preferred language for working with data mining because it is open source and it is really easy to learn and work with Python. Apart from that large community of active developers is actively working on python tools for data mining.
There are various tools developed by open source community for data mining. Most of them are completely free.
Pattern is one of the most famous used modules of data mining developed entirely using Python. It’s the ease of use that makes this module really popular. Also since it is open source, so anytime you can add your own components or modules or customize it according to your needs. The most significant feature of this module is that it is a complete package and you do not need to interface it with any external or other modules. This module includes following components:
- Data Mining tools(Web Crawler, Wikipedia+Twitter+Google API, HTML parser)
- Machine learning tools
- Text Analysis Tools(Word Net interface)
- Classification and Clustering(KNN)
- Natural Language Processing tools
- Network Analysis and Visualization tools(centrality of graphs)
Pandas solves the major problem that some of the Python tools face. Since most Python tools excel at data preparation and not at data modeling or analysis, this tool has excellent libraries that power it with efficient and productive data analysis capabilities.
This tool can also corroborate with other Python tools to assist them in proper analysis and make the mining of data more efficient. It helps user focus more on research than on programming aspect for using this tool. This elegance of this tool lies in simplicity of its API and its performance in analyzing high volume of data.
Some of its features:
- Efficient and high performance in joining data-sets
- Optimized pivoting and reshaping of data-sets
- Various tools for reading and writing data in various formats:text file, excel file, CSV format, database etc
- Fast data-frame object for indexing and data manipulation
MDP( Modular Toolkit for Data Processing)
Basically Modular Toolkit for Data Processing is a collection of some of the most widely used and popular algorithms for data processing that are combined together to increase their effectiveness and capability to process more complex data structures.
Some of the algorithms include:
- Independent Component Analysis(TDSEP, CuBICA, FastICA, and JADE)
- Principal Component Analysis(NIPALS and PCA)
- Slow Feature Analysis(SFA)
MDP includes a group of unsupervised and supervised algorithms that can be combined to form complex network structures. MDP is developed considering research on neural network and neuroscience but it is designed in such a way that it can be employed or used anywhere trainable data algorithms are preferred.
This open source tool is used for data analysis as well as visualization. It is a component based data analytics/mining software. Techniques employed by this tool in data mining include: preprocessing, exploration, data visualization and data modeling. It has a very nice and easy user interface for novice users.
There is also option of using Python programming language for advanced users. Various features this tool include: visualize data in hierarchical clustering form, can guess which widget to add to a developing schema, algorithm for mining association rules, features various plotting techniques including parallel coordinate plot, survey plot, network of music performers etc.
Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library is a module based Machine Learning and Data Mining Library of algorithms. The main feature of this tool is that even entry level students can work on this tool with great ease because high level programming knowledge is not required to use this tool.
It consists of algorithms for unsupervised and reinforced learning and overall all evolution using the entered dataset. It is open source and free for anyone to use. Its library is built around the basic structure and with properties of neural network.
It is a command shell or terminal that has enhanced capability for introspection with support for mathematical expressions and code. It has a powerful and typical structure for distributed and parallel computing.
It strongly supports parallelism in execution, debugging and development. There is a comprehensive list of IPython Notebook that prove to be extremely helpful in mining various social media sites including facebook, google+, twitter etc
Though lot of data mining tools are available on internet but not many of them are open source and have same capability and power in analyzing and dealing with data and what makes Python based tools different from others is most of them are open source with good documentation available for their use and most importantly an active community of its user that proves extremely helpful.