Frequent pattern mining can well extract insight from transaction patterns, and it is a desired capability for fully understanding the customer's purchase behavior. However, most of the algorithms are focus on the transverse relationship and the longitudinal analysis is missed. To address this defect, FP-ICA, a Frequent Pattern mining algorithm for Item-oriented and Customer-oriented Analysis is proposed. A pattern with its items occur in the same transaction is item-oriented, and a pattern with its items occur cross several transactions of a customer is customer-oriented. FP-ICA transforms the transactions to a bitmap which contains a header for recording customer information, and the frequent patterns are obtained by logic And-operation. Different mining rules are used for item-oriented and customer-oriented discovery. Experiments are conducted to demonstrate the fast speed achievement and good scalability of FP-ICA.