3.3 Generate rules using apriori algorithm.

Support threshold for users with orders was 2%, for others 1%.

Here is first, most strongest rule from each report.

All users without orders:

Sorted by lift.

supp	conf	lift	leve	Cove	rule
0.012	0.623	20.260	0.012	0.020	divertimento -> tecnologia

Sorted by leverage.

supp	conf	lift	leve	Cove	rule
0.036	0.523	7.215	0.031	0.068	pumpseopentoes -> botins

Sorted by confidence.

supp	conf	lift	leve	Cove	rule
0.012	0.854	12.525	0.011	0.014	sabrinasemocassins botins -> pumpseopentoes

Subscribed users:

Sorted by lift:

supp	conf	lift	leve	cove	rule
0.012	0.403	21.484	0.011	0.029	tecnologia -> divertimento

Sorted by leverage:

supp	conf	lift	leve	cove	rule
0.034	0.480	6.962	0.029	0.071	botins -> pumpseopentoes

Sorted by confidence. 

supp	conf	lift	leve	cove	rule
0.011	0.871	12.198	0.010	0.012	botasdesalto pumpseopentoes -> botins

Users with orders:

Sorted by lift

supp	conf	lift	leve	cove	rule
0.021	0.729	25.658	0.020	0.028	jogos -> divertimento tecnologia

Sorted by leverage

supp	conf	lift	leve	cove	rule
0.034	0.514	9.745	0.030	0.066	pumpseopentoes -> botins

Sorted by confidence.

supp	conf	lift	leve	cove	rule
0.021	0.921	23.219	0.020	0.022	jogos tecnologia -> divertimento

The strongest rules obtained using sorting by leverage. Because support, confidence, coverage and lift a also big enough. Very interesting results can be obtained using sorting by confidence, there are rules with more then 2 items. All 3 basket shows the same behavior of visitors: they are has seen and ordered many shoes.

SUMMARY.

We implemented association rules inducer using Orange framework and used it to get knowledge from dataset that “We-Commerce” gave us. Dataset was processed and analyzed, all noises was reduced and three relevant subsets of data was aggregated. The first was subset of all visitors, the second subset of visitors that has user_gui (was subscribed), the third subset of visitors that has at least one order. This data separating helps us to see common patterns of behavior for different types of visitors. All rules was sorted three times by three parameters: by lift, by confidence, by leverage. The most strongest rules shows sorting by leverage, because in thus report all other parameters relatively high, higher then the mean values. In other cases some of parameters may be lower then the mean.

Better subset was visitors with orders, because all rules induced from has highest leverage, most confidence more then 50% and support higher then 2%. While others has rules with support higher then 1%, and most of them between 1%-2%.

APPENDIX A.

CREATE VIEW v_commerce_without_noise_product AS

SELECT *

FROM commerce c

WHERE c.product_gui != 'open'

AND c.product_gui != 'home'

AND c.product_gui != ''

AND c.product_gui != '/onestepcheckout/'

AND c.product_gui != '/checkout/cart/index/'

AND c.product_gui != '/lon-about-us'

AND c.product_gui != '/lon-contacts'

AND c.product_gui != 'display.category*homepage'

AND c.product_gui != '/ljv-contacts'

AND c.product_gui != '/sales/order/history/'

AND c.product_gui not like '/customer%'

AND c.product_gui not like '/?gclid%'

AND c.product_gui not like '/?t%'

AND c.product_gui not like '/sales/order/view/%'

AND c.product_gui not like 'order.%'

AND c.product_gui not like 'orderonline%'

AND c.product_gui not like 'ordermailphone%'

AND c.product_gui not like 'pddtitle%'

AND c.product_gui not like 'pddimage%'

AND c.product_gui not like 'intro%'

;

CREATE VIEW v_visitors_with_orders AS

SELECT DISTINCT cookie_id FROM commerce

WHERE product_gui LIKE '%order%'

AND product_gui NOT LIKE 'display.%'

AND product_gui NOT LIKE '/sales/%'

OR product_gui LIKE '%checkout%'

;

CREATE VIEW v_num_of_events_per_transaction AS

SELECT cookie_id,session_id,count(*) number_of_events_per_session

FROM commerce

GROUP BY session_id,cookie_id

;

CREATE VIEW v_number_of_events_per_cookie AS

SELECT cookie_id,count(*) sessions_count, sum(number_of_events_per_session)

FROM v_num_of_events_per_transaction

GROUP BY cookie_id

ORDER BY cookie_id

;

CREATE VIEW v_count_of_visitors_and_sessions_per_event AS

SELECT number_of_events_per_session numberofeventspersession, count(number_of_events_per_session) numberofvisitors

FROM v_num_of_events_per_transaction

GROUP BY number_of_events_per_session

ORDER BY number_of_events_per_session ASC

;

CREATE VIEW v_count_of_visitors_and_sessions_per_cookie AS

SELECT sessions_count numberofsessions, count(*) numberofvisitors

FROM v_number_of_events_per_cookie

GROUP BY sessions_count

ORDER BY sessions_count

;

CREATE VIEW v_relevant_subset_cookies AS

SELECT DISTINCT cookie_id

FROM v_number_of_events_per_cookie

WHERE sessions_count<30

AND sessions_count>5

;

CREATE VIEW v_relevant_subset_without_orders_all AS

SELECT c.session_id,c.product_gui

FROM v_relevant_subset_cookies r,v_commerce_without_noise_product c

WHERE r.cookie_id = c.cookie_id

ORDER BY c.session_id

;

CREATE VIEW v_relevant_subset_without_orders_subscribed_users AS

SELECT c.session_id,c.product_gui

FROM v_relevant_subset_cookies r,v_commerce_without_noise_product c

WHERE r.cookie_id = c.cookie_id

AND c.user_gui != ''

ORDER BY c.session_id

;

CREATE VIEW v_count_of_products_in_relevant_subset_all AS

SELECT count(product_gui) number_of_product,product_gui product_name

FROM v_relevant_subset_without_orders_all

GROUP BY product_gui

ORDER BY number_of_product DESC

;

CREATE VIEW v_count_of_products_in_relevant_subset_subscribed_users AS

SELECT count(product_gui) number_of_product,product_gui product_name

FROM v_relevant_subset_without_orders_subscribed_users

GROUP BY product_gui

ORDER BY number_of_product DESC

;

CREATE VIEW v_relevant_subset_orders_only AS

SELECT p.session_id, p.product_gui

FROM v_visitors_with_orders o,v_commerce_without_noise_product p

WHERE o.cookie_id=p.cookie_id

ORDER BY p.session_id

;

COPY (SELECT * FROM v_relevant_subset_without_orders_all) TO '/sorted_relevant_subset_all.tab' csv header

;

COPY (SELECT * FROM v_relevant_subset_without_orders_subscribed_users) TO '/sorted_relevant_subset_subscribed.tab' csv header

;

COPY (SELECT * FROM v_relevant_subset_orders_only) TO '/sorted_relevant_subset_orders_only.tab' csv header

;