Question 1

What is data mining?

Accepted Answer

Data mining is the process of discovering patterns, correlations, anomalies, and insights in large datasets using machine learning and statistical methods such as Apriori, FP-Growth, Eclat, Decision Trees, Naive Bayes, KNN, and K-Means clustering.

Question 2

Is MineFlow free to use?

Accepted Answer

Yes. MineFlow is completely free — no signup, no subscription, no data upload. All processing happens in your browser.

Question 3

What algorithms does MineFlow support?

Accepted Answer

MineFlow supports 7 data mining algorithms: Apriori (association rules), FP-Growth (frequent patterns), Eclat (set intersection mining), Decision Tree (tree-based classification), Naive Bayes (probabilistic classification), KNN (distance-based classification), and K-Means (centroid-based clustering). It also includes TF-IDF and LDA for text mining.

Question 4

What file formats does MineFlow accept?

Accepted Answer

MineFlow accepts CSV and Excel (.xlsx/.xls) files for analysis and can export results to CSV, Excel, and HTML report formats.

Question 5

Is my data safe on MineFlow?

Accepted Answer

Yes — all data mining computations run 100% client-side in your browser. Your datasets are never uploaded to or stored on any server.

Question 6

What is K-Nearest Neighbors (KNN)?

Accepted Answer

KNN is a distance-based classification algorithm that classifies new data points by finding the K closest training examples and using majority voting. MineFlow uses Euclidean distance with automatic min-max normalization.

Question 7

What is K-Means clustering?

Accepted Answer

K-Means is an unsupervised learning algorithm that partitions data into K clusters. MineFlow uses K-Means++ initialization for better convergence and provides cluster assignments, centroids, and inertia scores.