Day 75 implementation of sequential pattern mining

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okay, let's dive into implementing day 75, focusing on sequential pattern mining. we'll cover the theory, a popular algorithm (prefixspan), its implementation in python, explanations, and examples. this will be a comprehensive guide to get you started.

**day 75: sequential pattern mining**

**1. what is sequential pattern mining?**

sequential pattern mining is a data mining technique used to discover frequently occurring subsequences as patterns in a sequence database. it finds recurring orderings of events or items, allowing you to predict future events or understand trends within a sequence of data.

* **sequence database:** a collection of sequences. each sequence is an ordered list of events or transactions.
* **sequence:** an ordered list of itemsets. itemsets within a sequence maintain their order.
* **itemset:** a set of items that occur together at the same time. items within an itemset are unordered.
* **subsequence:** a sequence contained within another sequence. for example, `a(bc)d(ef)` contains the subsequence `a(bc)d`.
* **support:** the proportion of sequences in the database that contain a particular subsequence. it measures the frequency of occurrence.
* **minimum support (minsup):** a threshold that defines the minimum frequency required for a subsequence to be considered frequent or significant.
* **frequent sequence (pattern):** a sequence with support greater than or equal to the minimum support.

**example:**

imagine customer purchase data from an online store.

* **sequence database:**

* customer 1: `(milk, bread)(beer)(diapers)`
* customer 2: `(milk)(bread, diapers)(beer)`
* customer 3: `(milk)(bread)(diapers)`
* customer 4: `(milk, bread)(diapers)`

* **sequences:** each customer's purchase history is a sequence.
* **itemsets:** for customer 1, `(milk, bread)` is an itemset, `(beer)` is an itemset, and `(diapers)` is an itemset.
* **subsequence:** ` (milk)(diapers) ` is a ...

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sequential pattern mining
Day 75 implementation
data mining
pattern discovery
algorithm optimization
time-series analysis
frequent pattern growth
data sequence analysis
mining algorithms
temporal data mining
pattern recognition
data analytics
machine learning
workflow automation
data visualization

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