IBM Watson Explorer vs Weka comparison

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IBM Watson Explorer

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Executive SummaryUpdated on May 21, 2025

Weka and IBM Watson Explorer are competing in data analysis and AI technologies. Data comparisons show IBM Watson Explorer is often preferred for its comprehensive feature set.

Features:Weka integrates diverse machine learning algorithms and provides robust data preprocessing tools. Its user-friendly interface and open-source flexibility make it ideal for academic use. IBM Watson Explorer excels in text analytics, cognitive computing, and versatile functionality for enterprise solutions.

Room for Improvement:Weka could enhance its visualization capabilities and support for large data volumes. Its deployment options might lack scalability for bigger enterprises. More user support resources could be beneficial. IBM Watson Explorer might improve in user interface complexity and resource requirements. Enhancements in integration options and a more straightforward setup could be beneficial.

Ease of Deployment and Customer Service:Weka offers a straightforward deployment suitable for smaller projects with a simple setup. IBM Watson Explorer requires a more complex enterprise-level deployment but has robust customer support and comprehensive documentation.

Pricing and ROI:Weka's open-source model provides lower setup costs, appealing to cost-sensitive projects, delivering good ROI through its flexibility. IBM Watson Explorer comes with higher setup costs, justified by its advanced capabilities and substantial ROI for enterprises able to invest in its extended features.

To learn more, read our detailed IBM Watson Explorer vs. Weka Report (Updated: December 2025).

IBM Watson Explorer vs. Weka

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

IBM Watson Explorer

Ranking in Data Mining

9th

Average Rating

8.4

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Weka

Ranking in Data Mining

4th

Average Rating

7.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Anomaly Detection Tools (2nd)

Mindshare comparison

As of January 2026, in the Data Mining category, the mindshare of IBM Watson Explorer is 2.6%, up from 0.7% compared to the previous year. The mindshare of Weka is 9.7%, down from 21.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining Market Share Distribution
Product	Market Share (%)
Weka	9.7%
IBM Watson Explorer	2.6%
Other	87.7%

Data Mining

Featured Reviews

it_user1319820

Lead Engineer at a computer software company with 10,001+ employees

A data analysis tool that is scalable and includes keyword search functionality

The solution is used for a government company for data collection and analysis I have found the auto-generated document very useful as well as the main keywords that are highlighted, which are used for the search functionality within IBM Watson Explorer. I have been using the solution for five…

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Xavier Suriol

Manager at XS AMSAFIS DATASETS, S.L.

A good solution offering a range of tools but is limited by its user-handling capacities

In a new machine learning job, if the method is a bit foreign to me, if I have to do it in R, it could be a tedious task. First, I need to identify the libraries required for the new methodology. This can involve identifying two, three, or even four libraries. Then, I need to read their manuals thoroughly. This is time-consuming. In Weka, as all machine learning tools are on my desktop, I easily find out the method. As a freelancer, people send me datasets, and I work on the statistics at home before providing the solution. When a solution needs to be implemented on a server, server programmers install it on the server. This is similar to Power BI, where I prepare files on my desktop, and someone else uploads them to the server for others to access. I think I cannot send a Weka solution to a server programmer. In Weka, anyone can run the program without being a programmer, which is a good feature since the entry cost is very low.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"For me, as a user, the most valuable feature is the ability to ingest and then retrieve information from a range of separate sources; the ability to dissect questions in context and actually answer them."

"I have found the auto-generated document very useful as well as the main keywords that are highlighted, which are used for the search functionality within IBM Watson Explorer."

"The valuable feature of Watson Explorer for us is data entities, and to see the hidden insights from within unstructured data."

"Ease of use is pretty good as is the standardization of not actually having to have my own natural learning algorithms, just to use the Watson APIs."

"We take natural language that was happening in our repositories and our application and then feed it to the Watson APIs. We receive JSON payloads as an API response to get cognitive feedback from the repository data."

"The ability to easily pull together lots of different pieces of information and drill down in a smarter way than has been possible with other analytics tools is key. Watson is all based on a set of AI and deep learning, machine-learning capabilities, and it is looking behind the scenes at some relationships that you likely would not have spotted on your own. It's pulling things together, categorizing some things, that are not something that you might have seen on your own."

"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."

"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."

"Working with complicated algorithms in huge datasets is really easy in Weka."

"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."

"The interface is very good, and the algorithms are the very best."

"There are many options where you can fill all of the data pre-processing options that you can implement when you're importing the data. You can also normalize the data and standardize it in an easier way."

"Weka is a very nice tool, it needs very small requirements. If I want to implement something in Python, I need a lot of memory and space but Weka is very lightweight. Anyone can implement any kind of algorithm, and we can show the results immediately to the client using the one-page feature. The client always wants to know the story. They want the result."

"The path of machine learning in classification and clustering is useful. The GUI can get you results. No programming is needed. No need to write down your script first or send to your model or input your data."

More Weka pros

Cons

"Much of IBM operates this way, where they have sets of tools that are in the middleware space, and it becomes the customer's responsibility or the business partner's responsibility to develop full solutions that take advantage of that middleware. I think IBM's finding itself in that spot with Watson-related technologies as well, where the capabilities to do really interesting and useful things for customers is there, but somebody still has to build it. Is that going to be the customer? Are they going to be willing to take on that responsibility themselves"

"Stability is actually one of the areas that could use improvement. Setting it up is always tough. Setting Explorer requires experts, but also the underlying platform is not that stable. So it really needs a good expert to keep it running."

"Small businesses will probably have a little harder time getting into it, just because of the amount of resources that they have available, both financial and time, but it really is a solution that should work for them."

"The solution is expensive."

"More cognitive feedback would be good. The natural language analysis is great, the sentiment analyzers are great. But I would just like to see more... innovation done with the Watson platform."

"It is a little bit tricky to get used to the workflow of knowing how to train Watson, what can be provided, what can't be, how to provide it, how to import, export, and what it means every time you have to add a new dictionary"

"I would say, give some kind of a community edition, a free edition. A lot of companies do, even Amazon gives you some kind of trial and error opportunities. If they could provide something like that, it would be good."

"It needs better language support, to include some other languages. Also, they should improve the user interface."

"In terms of scalability, I think Weka is not prepared to handle a large number of users."

"Weka could be more stable."

"The visualization of Weka is subpar and could improve. Machine learning and visualization do not work well together. For example, we want to know how we can we delete empty cells or how can we fill in the empty cells without cleaning the data system and putting it together."

"I believe is there are a few newer algorithms that are not present in the Weka libraries. Whereas, for example, if I want to have a solution that involves deep learning, so I don't think that Weka has that capability. So in that case I have to use Python for ... predict any algorithms based on deep learning."

"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."

"If you have one missing value in your dataset and this missing value belongs to a specific attribute and the attribute is a numeric attribute and there is only one missing data, whenever you import this data, the problem is that Weka cannot understand that this is a numeric field. It converts everything into a string, and there is no way to convert the string into numerical math. It's really very complicated."

"A few people said it became slow after a while."

"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."

More Weka cons

Pricing and Cost Advice

"The solution is expensive."

"The solution is free and open-source."

"As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if it is a commercial product."

"We use the free version now. My faculty is very small."

"Currently, I am using an open-source version so I don't know much about the price of this solution."

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Top Industries

By visitors reading reviews

Performing Arts

12%

University

11%

Healthcare Company

Recreational Facilities/Services Company

Educational Organization

16%

University

14%

Computer Software Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	2
Midsize Enterprise	2
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	7
Midsize Enterprise	1
Large Enterprise	2

Questions from the Community

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What do you like most about Weka?

It is a stable product.

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What is your experience regarding pricing and costs for Weka?

Weka is free and open-source software. That is why I used it over KNIME.

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What needs improvement with Weka?

I haven't found it particularly useful. It lacks state-of-the-art algorithms and impressive outcomes. While it might offer insights for basic warehouse tasks, it falls short of deeper understanding...

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Product Reports

IBM Watson Explorer

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Also Known As

IBM WEX

No data available

Overview

IBM Watson Explorer is a cognitive exploration and content analysis platform that lets you listen to your data for advice. Explore and analyze structured, unstructured, internal, external and public content to uncover trends and patterns that improve decision-making, customer service and ROI. Leverage built-in cognitive capabilities powered by machine learning models, natural language processing and next-generation APIs to unlock hidden value in all your data. Gain a secure 360-degree view of customers, in context, to deliver better experiences for your clients.

IBM

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

Sample Customers

RIMAC, Westpac New Zealand, Toyota Financial Services, Swiss Re, Akershus University Hospital, Korean Air Lines, Mizuho Bank, Honda

Information Not Available

IBM Watson Explorer vs. Weka