FP-tree Based On The Frequent Frequent Co-location Pattern Mining

Many different areas, such as industrial, commercial, public health, geology, transportation and other domains will generate large volumes of data every day, we may derive many potentially meaningful patterns that will offers rather valuable reference information for these fields from these data. By collecting and analyzing these data, we’ve got a number of applications in some fields, such as{beer, bread} are often purchased together in a large supermarket, through the development of marketing strategies or commodities placement on the shelf, supermarket may sell more and get more benefits. Currently, space co-location pattern mining is a hot research topic in the field of data mining.A co-location pattern is a set of some spatial features which regularly appeared together in the same space. A frequent schema is a set of items which regularly appears in the same transaction database. With the research in recent years, frequent pattern mining has undergone a big change from generation-test based algorithms to projection based algorithms. Co-location modes and frequent modes are similar in many ways, while the lack of transaction concept which makes some of the frequent pattern mining algorithms are difficult to directly used in co-location pattern. This paper investigates some FP-tree based frequent schema mining algorithms,CM algorithm and FC algorithm, adopts the advance of these algorithms, we proposed a common framework which mines maximum frequent co-location pattern and gives its own algorithm, through analysis, the new algorithm is correct, complete and efficient, experimental results also turned out that the algorithm in this paper outperforms the candidate generation-test based collocation miner by an order of magnitude.