The true advantages with effective data mining are those that we are all aware of, but finding the right tools and using them efficiently to derive business value has been a cause for concern. Going frugal has been deemed the way to go when it comes to using tools and practices and in this article CodeForce 360 presents some of the most widely used open- source data mining tools that are an absolute must for datasmiths across the tech realm.
- 1. R: When it comes to open-source data mining tools, R is definitely among the top tier. It’s slick, it’s smooth, and it’s user-friendly. Moreover, the best part about R is the constant availability of pre-built packages which could be used to run high-end programs against even the most complicated of data pools.
- 2. RapidMiner: Along with R, RapidMiner is definitely among the go-to tools for effective data mining. This tool is easily adaptable for java programmers while boasting an operator framework defined by learning operators to work on large data sets and even arbitrary processes.
- 3. Python: A general purpose programming language which is considered to be in the same boat as R since it’s extremely user-friendly. However, most users prefer Python over R at times due to its sheer flexibility with data structures. Working on python is often addictive since you can create extremely large data pools effortlessly and perform even the most complicated of operations in no time.
- 4. KNIME: Short for “Konstanz Information Miner”, this tool is among the most popular out there as it’s built on a java platform which makes it compatible with Windows, Mac etc. A major advantage with KNIME is that it doesn’t require a great deal of experience in programming to handle it. KNIME’s trump card is its smart UI with a visual podium that performs data exploration and processing, seamlessly.
- 5. Spark: The most talked about open-source tools in the tech realm today which is being used by bluechip companies across the world from Ebay to NASA for handling gigantic data sets at breath-taking efficiency.
- 6. H2O: A technically state-of-the-art tool which is backed by excellent data indexing capabilities and extremely effective algorithms for data mining. While the platform itself is impeccably designed, you can also team up this platform with multiple APIs such as the R API which can be used to skim through huge data sets and clusters, effortlessly.
- 7. Orange: The best tool out there for programmers who thrive on all things python. With built-in components for machine learning, extensions for bioinformatics, and a whole set of functions for data processing, Orange is extremely user-friendly and is even suitable to those fresh to the world of analytics.
According to CodeForce 360, these tools are highly essential if you’re looking to perform scores of data/insight-centric tasks such as predicting customer behaviour, improving product performance and quality, deciphering patterns, and more. Furthermore, it’s up to smart data mining professionals to apply their skills across various tools to find the tool which is ideal for carrying out operations against extremely large data sets and more.
Looking for solutions and trained hands in niche data mining technologies? Contact CodeForce 360, today!