This paper describes Natural Language Processing techniques to extract patterns of destination image formation from Social Media postings. In a pilot study, we show how Twitter postings can be mined to uncover travel motivations and values for various travel destinations; we also explore the mental representations that underly these values and motivations. While this pilot study is subject to limitations, the results demonstrate that Twitter data is a compelling source of information about travellers and destinations.

Since the Free Trade Agreements (FTAs) with Chile, the EU, and the U.S., Korean agricultural produce markets have turned into a fierce competition landscape. Under these competitive circumstances, marketing is critical. The objective of the research presented herein is to understand the characteristics of customer preferences after locating trends of purchased items. So This research establishes sustainable strategies for Korean agricultural produce. This investigation used market-basket analysis techniques and panel data for its research. Market-basket analysis is a technique which attempts to find groups of items that are commonly found together. The results show that, for one year, processed food using wheat, processed marine products, and pork are commonly bought together and that yogurt and milk also are bought together. The characteristics of customers buying these items are 44 years old and live in a four-person household with two children. These customers do not live with their parents.

  A process mining is considered to support the discovery of business process for unstructured process model, and a process mining algorithm by using the associated rule and sequence pattern of data mining is developed to extract information about process

In this paper, we address a mining association rules with weighted items and multiple minimum support. We generalize this to the case where items are given weights to reflect their importance to the user. And to find rules that involve both frequent and rare items, we specify multiple minimum supports to reflect the frequency of the items. In rule mining, different rules may need to satisfy different minimum supports depending on what items are in the rules.

We study data mining technique in an electronic commerce. Customers travel web pages in an shopping mall and they sometimes purchase products. It is important for a web master in a shopping mall to know customer's purchasing patterns. We discover both association rules among customer's purchasing products and customer's traversal paths. We propose three phase mining technique to explore it. In the first phase, it find large items from sales database. In the second phase, it add to traversal paths. In the third phase, it discover associations rules from large items.