পদ্ধতির তুলনা করুন

	ক্রমিক বিন্যাস খনন (Sequential Pattern Mining)	FP-Growth (ফ্রিকোয়েন্ট প্যাটার্ন গ্রোথ)	প্রসেস মাইনিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	প্রক্রিয়া খনন
পরিবার	Machine learning	Machine learning	Process / pipeline
উদ্ভবের বছর	1995	2000	2016
প্রবর্তক	Rakesh Agrawal & Ramakrishnan Srikant	Jiawei Han, Jian Pei & Yiwen Yin	Wil van der Aalst
ধরন	Unsupervised pattern discovery	Frequent-itemset mining algorithm	Data-driven process analysis technique
মৌলিক উৎস	Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. IEEE International Conference on Data Engineering (ICDE), 3–14.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action (2nd ed.). Springer. ISBN: 978-3-662-49850-7
অপর নাম	Sequence Pattern Mining, Sequential Data Mining, Temporal Pattern Mining, Ardışık Örüntü Madenciliği	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	Workflow Mining, Event Log Analysis, Process Discovery, Süreç Madenciliği
সম্পর্কিত	3	4	2
সারসংক্ষেপ	Sequential Pattern Mining discovers ordered patterns that recur across multiple event sequences in a database. Introduced by Agrawal and Srikant in 1995, it extends association-rule mining to time-ordered transactions. A pattern is frequent when it appears as an ordered subsequence in at least a user-specified fraction of all sequences. The method is widely applied wherever the order of events carries meaning, such as customer purchase histories, clickstream logs, electronic health records, and DNA sequence analysis.	FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets.	Process Mining is a data-driven discipline that extracts knowledge about real-world processes from event logs recorded by information systems. Introduced systematically by Wil van der Aalst, with foundational workflow mining formalized in 2004 and consolidated in the 2016 textbook, the technique bridges data science and process management. It enables organizations to discover how processes actually execute, check whether execution conforms to prescribed models, and diagnose performance bottlenecks — all directly from digital traces.