Data Mining is important for Business Analytics. Data Mining is the process of finding valuable information from large amount of data. In data mining the raw data becomes input, the data mining activity is the task and the output is actionable data. Data mining tools are software programs that are used to in framing and executing processing techniques. Following are the list of most widely used data mining tools.
1. Rapid Miner
2. SPSS
3. Weka
4. Oracle data mining
5. Teradata
6. Python
7. Kaggle
8. Rattle
Rapid Miner:
Rapid miner is an integrated environment for data processing. Rapid miner is developed in Java language. Rapid miner is used in various stages of data mining such as data preprocessing, exploratory data analysis, data visualization etc. rapid miner is helpful in machine learning, deep learning, text mining and predictive analytics.
SPSS:
IBM SPSS Modeler is a modern data visualization tool. It’s a visual data science and machine learning solution. SPSS Modeler is used in data preparation, predictive analytics, model management and deployment.
Weka:
Waikato Environment for Knowledge Analysis (Weka) could also be a set of machine learning tools written in Java. A collection of visualization tools for predictive modeling during a GUI presentation, helping you build your data models and test them, observing the model performances graphically.
Oracle Data Mining:
Oracle, the planet leader in database software, combines it’s prowess in database technologies with Analytical tools and brings you Oracle Advanced Analytics Database a part of the Oracle Enterprise Edition. It features several data processing algorithms for classification, regressing, prediction, anomaly detection and more. This is proprietary software and is supported by Oracle technical staff in helping your business build a strong data processing infrastructure at the enterprise scale.
The algorithms integrate directly with Oracle database kernel and operate natively on data stored in its own database, eliminating the necessity for extraction of knowledge into standalone analytics servers. The Oracle Data Miner provides GUI tools taking the user through the method of making, testing and applying data models
Teradata:
A cloud data analytics platform marketing its no code required tools during a comprehensive package offering enterprise-scale solutions. With Vantage Analyst, you don’t got to be a programmer to code complex machine learning algorithms. A simple GUI based system for quick enterprise-wide adoption.
Python:
Python may be a freely available and open-source language that's known to possess a fast learning curve. Combined with is that the ability as a general-purpose language and it's an outsized library of packages that help build a system for creating data models from the scratch, Python makes for an excellent tool for organizations who want the software they use to be custom built to their specifications.
With Python, you won’t get the flamboyant stuff that proprietary software offers, but the functionality is there for anybody to select up and creates their own environment with graphical interfaces of their liking. What also supports python is that the large online community of package developers who make sure the packages on offer are robust and secure. One of the features Python is understood for during this field is powerful on the fly visualization features it offers.
Kaggle:
The largest community of knowledge scientists and machine learning professionals. Kaggle although started as a platform for machine learning competitions, is now extending its footprint into the overall public cloud-based data science platform arena. Kaggle now offers code and data that you simply need for your data science implementations. There are over 50k public datasets and 400k public notebooks that you simply can use to build up your data processing efforts. The huge online community that Kaggle enjoys is your safety net for implementation-specific challenges.
Rattle
The rattle is an R language-based GUI tool for data processing requirements. The tool is free and open-source and may be wont to get statistical and visual summaries of knowledge, the transformation of knowledge for data models, build supervised and unsupervised machine learning models and compare model performance graphically.
So, there you've got it, a powerful list of comprehensive tools and frameworks that assist you build a knowledge ecosystem for building, testing and implementing data models that enable you to derive value out of your data at enterprise out of your data at enterprise scale.
