Prime-based method for interactive mining of frequent patterns

Over the past decade, an increasing number of efficient mining algorithms have been proposed to mine the frequent patterns by satisfying a user specified threshold called minimum support (minsup). However, determining an appropriate value for minsup to find proper frequent patterns in different applications is extremely difficult. Since rerunning the mining algorithms from scratch can be very time consuming, researchers have introduced interactive mining to find proper patterns by using the current mining model with various minsup. Thus far, a few efficient interactive mining algorithms have been proposed. However, their runtime do not fulfill the need of short runtime in real time applications especially where data is sparse and proper frequent patterns are mined with very low values of minsup. As response to the above-mentioned challenges, this study is devoted towards developing an interactive mining method based on prime number and its special characteristic “uniqueness” by which the content of the relevant data is transformed into a compact layout. At first, a general architecture for interactive mining is proposed consisting of two isolated components: mining model and mining process. Then, the proposed method is developed based on the architecture such that the mining model is constructed once, and it can be frequently mined by various minsup. In the mining model construction, the content of relevant data is captured by a novel tree structure called PC-tree with one database scan and mining materials are consequently formed. The PC-tree is a well-organized tree structure, which is systematically built based on descendant making introduced in this study. Moreover, this study introduces a mining algorithm called PC-miner to mine the mining model frequently with various values of minsup. It grows an effective candidate head set introduced in this study starting from the longest candidate patterns by using the Apriori principle. Meanwhile, during the growing of the candidate head set in each round, the longest candidate patterns are used to find maximal frequent patterns from which the frequent patterns can be derived. Moreover, the PC-miner reduces the number of candidate patterns and comparisons by using several pruning techniques. A comprehensive experimental analysis is conducted by several experiments and scenarios to evaluate the correctness and effectiveness of the proposed method especially for interactive mining. The experimental results verify that the proposed method constructs the mining model independent of minsup once and this enable the model to be frequently mined. The results also show that the proposed method mines frequent patterns correctly and efficiently. Moreover, the results verify that the proposed method speeds up interactive mining of frequent patterns over both sparse and dense datasets with more scalable total runtime for very low values of minsup over sparse datasets as compared to results from the previous work

Concept-based Interactive Query Expansion Support Tool (CIQUEST)

This report describes a three-year project (2000-03) undertaken in the Information Studies Department at The University of Sheffield and funded by Resource, The Council for Museums, Archives and Libraries. The overall aim of the research was to provide user support for query formulation and reformulation in searching large-scale textual resources including those of the World Wide Web. More specifically the objectives were: to investigate and evaluate methods for the automatic generation and organisation of concepts derived from retrieved document sets, based on statistical methods for term weighting; and to conduct user-based evaluations on the understanding, presentation and retrieval effectiveness of concept structures in selecting candidate terms for interactive query expansion. The TREC test collection formed the basis for the seven evaluative experiments conducted in the course of the project. These formed four distinct phases in the project plan. In the first phase, a series of experiments was conducted to investigate further techniques for concept derivation and hierarchical organisation and structure. The second phase was concerned with user-based validation of the concept structures. Results of phases 1 and 2 informed on the design of the test system and the user interface was developed in phase 3. The final phase entailed a user-based summative evaluation of the CiQuest system. The main findings demonstrate that concept hierarchies can effectively be generated from sets of retrieved documents and displayed to searchers in a meaningful way. The approach provides the searcher with an overview of the contents of the retrieved documents, which in turn facilitates the viewing of documents and selection of the most relevant ones. Concept hierarchies are a good source of terms for query expansion and can improve precision. The extraction of descriptive phrases as an alternative source of terms was also effective. With respect to presentation, cascading menus were easy to browse for selecting terms and for viewing documents. In conclusion the project dissemination programme and future work are outlined

Performance and scalability of indexed subgraph query processing methods

Graph data management systems have become very popular as graphs are the natural data model for many applications. One of the main problems addressed by these systems is subgraph query processing; i.e., given a query graph, return all graphs that contain the query. The naive method for processing such queries is to perform a subgraph isomorphism test against each graph in the dataset. This obviously does not scale, as subgraph isomorphism is NP-Complete. Thus, many indexing methods have been proposed to reduce the number of candidate graphs that have to underpass the subgraph isomorphism test. In this paper, we identify a set of key factors-parameters, that influence the performance of related methods: namely, the number of nodes per graph, the graph density, the number of distinct labels, the number of graphs in the dataset, and the query graph size. We then conduct comprehensive and systematic experiments that analyze the sensitivity of the various methods on the values of the key parameters. Our aims are twofold: first to derive conclusions about the algorithms’ relative performance, and, second, to stress-test all algorithms, deriving insights as to their scalability, and highlight how both performance and scalability depend on the above factors. We choose six wellestablished indexing methods, namely Grapes, CT-Index, GraphGrepSX, gIndex, Tree+∆, and gCode, as representative approaches of the overall design space, including the most recent and best performing methods. We report on their index construction time and index size, and on query processing performance in terms of time and false positive ratio. We employ both real and synthetic datasets. Specifi- cally, four real datasets of different characteristics are used: AIDS, PDBS, PCM, and PPI. In addition, we generate a large number of synthetic graph datasets, empowering us to systematically study the algorithms’ performance and scalability versus the aforementioned key parameters

Making visible the invisible through the analysis of acknowledgements in the humanities

Purpose: Science is subject to a normative structure that includes how the contributions and interactions between scientists are rewarded. Authorship and citations have been the key elements within the reward system of science, whereas acknowledgements, despite being a well-established element in scholarly communication, have not received the same attention. This paper aims to put forward the bearing of acknowledgements in the humanities to bring to the foreground contributions and interactions that, otherwise, would remain invisible through traditional indicators of research performance. Design/methodology/approach: The study provides a comprehensive framework to understanding acknowledgements as part of the reward system with a special focus on its value in the humanities as a reflection of intellectual indebtedness. The distinctive features of research in the humanities are outlined and the role of acknowledgements as a source of contributorship information is reviewed to support these assumptions. Findings: Peer interactive communication is the prevailing support thanked in the acknowledgements of humanities, so the notion of acknowledgements as super-citations can make special sense in this area. Since single-authored papers still predominate as publishing pattern in this domain, the study of acknowledgements might help to understand social interactions and intellectual influences that lie behind a piece of research and are not visible through authorship. Originality/value: Previous works have proposed and explored the prevailing acknowledgement types by domain. This paper focuses on the humanities to show the role of acknowledgements within the reward system and highlight publication patterns and inherent research features which make acknowledgements particularly interesting in the area as reflection of the socio-cognitive structure of research.Comment: 14 page