Rule Mining Model Research Articles

Application of Association Rule Mining for Exploring the Relationship between Urban Land Surface Temperature and Biophysical/Social Parameters

This paper explores the relationship between remote sensing measurements of land surface temperature and biophysical/socioeconomic data by utilizing the association rule mining technique. The surfaces associated with urban uses typically radiate more heat as compared to its rural counterparts. There is a need to quantitatively analyze this contrast in temperature and the biophysical and social characteristics which influence it. Furthermore, in order to consider the urban heat island (UHI) effect, a parameterization is required to account for the urban surface characteristics impacts on the magnitude of land surface temperature (LST). The association rule mining model has demonstrated to bring in additional quantitative information concerning the relationships among urban parameters. The ASTER data from 2000 was used for the selection of appropriate variables to be used in the model. This information was then used for generating association rules between land-use land-cover (LULC) and LST information from 2000, 2001, and 2004. The results thus obtained quantitatively described the relationships between various urban parameters. It was found that there was little change in the percentage area of the LULC types from 2000 to 2004. This made the comparison of the results possible. In the case of the 2000 data, it was found that forest and impervious surfaces had strong association with temperature and scaled normalized difference vegetation index (SNDVI). Specific zones such as hospitals and universities had negative association with water. The comparison of data from 2000, 2001, and 2004 suggests that impervious surface and the zoning category of airport had a strong association. Nevertheless, the information extracted needs to be analyzed in greater detail in order to arrive at robust decision rules. Overall, the model so developed has demonstrated to be effective in predicting associations between urban LST and pertinent factors. This model could be useful for urban planners and environmental managers in quantifying rules that characterize a particular urban landscape.

Mining related queries from Web search engine query logs using an improved association rule mining model

With the overwhelming volume of information, the task of finding relevant information on a given topic on the Web is becoming increasingly difficult. Web search engines hence become one of the most...

Mining related queries from Web search engine query logs using an improved association rule mining model

AbstractWith the overwhelming volume of information, the task of finding relevant information on a given topic on the Web is becoming increasingly difficult. Web search engines hence become one of the most popular solutions available on the Web. However, it has never been easy for novice users to organize and represent their information needs using simple queries. Users have to keep modifying their input queries until they get expected results. Therefore, it is often desirable for search engines to give suggestions on related queries to users. Besides, by identifying those related queries, search engines can potentially perform optimizations on their systems, such as query expansion and file indexing. In this work we propose a method that suggests a list of related queries given an initial input query. The related queries are based in the query log of previously submitted queries by human users, which can be identified using an enhanced model of association rules. Users can utilize the suggested related queries to tune or redirect the search process. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness. Unlike many other rival techniques, it also performs reasonably well on less frequent input queries.

Mining for Mutually Exclusive Items in Transaction Databases

Association rule mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rule mining model have been proposed so far; however, the problem of mining for mutually exclusive items has not been directly tackled yet. Such information could be useful in various cases (e.g., when the expression of a gene excludes the expression of another), or it can be used as a serious hint in order to reveal inherent taxonomical information. In this article, we address the problem of mining pairs of items, such that the presence of one excludes the other. First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we test on transaction data.

A UML 2.0 profile to design Association Rule mining models in the multidimensional conceptual modeling of data warehouses

By using data mining techniques, the data stored in a Data Warehouse (DW) can be analyzed for the purpose of uncovering and predicting hidden patterns within the data. So far, different approaches have been proposed to accomplish the conceptual design of DWs by following the multidimensional (MD) modeling paradigm. In previous work, we have proposed a UML profile for DWs enabling the specification of main MD properties at conceptual level. This paper presents a novel approach to integrating data mining models into multidimensional models in order to accomplish the conceptual design of DWs with Association Rules (AR). To this goal, we extend our previous work by providing another UML profile that allows us to specify Association Rules mining models for DW at conceptual level in a clear and expressive way. The main advantage of our proposal is that the Association Rules rely on the goals and user requirements of the Data Warehouse, instead of the traditional method of specifying Association Rules by considering only the final database implementation structures such as tables, rows or columns. In this way, ARs are specified in the early stages of a DW project, thus reducing the development time and cost. Finally, in order to show the benefits of our approach, we have implemented the specified Association Rules on a commercial database management server.

Exploiting edge semantics in citation graphs using efficient, vertical ARM

Graphs are increasingly becoming a vital source of information within which a great deal of semantics is embedded. As the size of available graphs increases, our ability to arrive at the embedded semantics grows into a much more complicated task. One form of important hidden semantics is that which is embedded in the edges of directed graphs. Citation graphs serve as a good example in this context. This paper attempts to understand temporal aspects in publication trends through citation graphs, by identifying patterns in the subject matters of scientific publications using an efficient, vertical association rule mining model. Such patterns can (a) indicate subject-matter evolutionary history, (b) highlight subject-matter future extensions, and (c) give insights on the potential effects of current research on future research. We highlight our major differences with previous work in the areas of graph mining, citation mining, and Web-structure mining, propose an efficient vertical data representation model, introduce a new subjective interestingness measure for evaluating patterns with a special focus on those patterns that signify strong associations between properties of cited papers and citing papers, and present an efficient algorithm for the purpose of discovering rules of interest followed by a detailed experimental analysis.