# multiprefixspan Release 0.0.1

Implementation of Prefixspan for multiple items

Keywords
machine, learning, data, mining, sequential, pattern, exploration
MIT
Install
``` pip install multiprefixspan==0.0.1 ```

# MultiPrefixspan

Implement prefixspan algorithm for a sequence with multiple-item events, also with single0-item event.

## Prefixspan

MultiPrefixspan is to implement prefixspan algorithm for a sequence with multiple-item events.

## Installation

Using pip:

``````pip install multiprefixspan
``````

## Example for single event

Given data as follows, each element is a sequence.

``````db = [
[0, 1, 2, 3, 4],
[1, 1, 1, 3, 4],
[2, 1, 2, 2, 0],
[1, 1, 1, 2, 2],
]
``````

Execute prefixspan algorithm to find frequent patterns with the minimum support greater than or equal to 2 on db by:

``````from multiprefixspan import prefixspan_one_item_one_event

pre_object = prefixspan_one_item_one_event(db, 2)
pre_object.exect()
``````

Show result:

``````pre_object.found_patterns
``````
``````{'[0]': 2,
'[1]': 4,
'[1, 2]': 3,
'[1, 2, 2]': 2,
'[1, 3]': 2,
'[1, 3, 4]': 2,
'[1, 4]': 2,
'[1, 1]': 2,
'[1, 1, 1]': 2,
'[2]': 3,
'[2, 2]': 2,
'[3]': 2,
'[3, 4]': 2,
'[4]': 2}
``````

## Example for multiple events

Given data as follows, each element is a sequence, each element in a sequence may have more than one items.

``````db = [[{'a'}, {'a', 'b', 'c'}, {'a', 'c'}, {'d'}, {'c', 'f'}],
[{'a', 'd'}, {'c'}, {'b', 'c'}, {'a', 'e'}],
[{'e', 'f'}, {'a', 'b'}, {'d', 'f'}, {'c'}, {'b'}],
[{'e', 'g'}, {'a', 'f'}, {'c'}, {'b'}, {'c'}],
[{'a'}, {'g', 'h'}, {'f', 'e'}, {'b', 'c', 'e'}, {'a', 'b', 'c'}]
]
``````

Execute prefixspan algorithm to find frequent patterns with the minimum support greater than or equal to 2 on db by:

``````from multiprefixspan import prefixspan_multiple_items_one_event

pre_object = prefixspan_multiple_items_one_event(db, 2)
pre_object.exect()
``````

Show the first ten patterns (unordered):

``````pre_object.found_patterns[:10]
``````
``````[([{'f'}], 4),
([{'f'}, {'a'}], 2),
([{'f'}, {'a', 'b'}], 2),
([{'e', 'f'}], 2),
([{'e', 'f'}, {'a'}], 2),
([{'e', 'f'}, {'a', 'b'}], 2),
([{'e', 'f'}, {'b'}], 2),
([{'e', 'f'}, {'b'}, {'b'}], 2)]
``````

Please refer to Demonstrate.ipynb for real data example.

## Reference

1. Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., & Hsu, M. (2001). PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth. Proceedings 17th International Conference on Data Engineering, 215-224.
2. Real data BMS1_spmf.txt from http://www.philippe-fournier-viger.com/spmf/index.php?link=datasets.php; BMSWebView1 (Gazelle) ( KDD CUP 2000). This dataset contains 59,601 sequences of clickstream data from an e-commerce. It contains 497 distinct items. The average length of sequences is 2.42 items with a standard deviation of 3.22. In this dataset, there are some long sequences. For example, 318 sequences contains more than 20 items.