Efficient Analysis of Pattern and Association Rule Mining Approaches

The process of data mining produces various patterns from a given data source. The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules. Numerous efficient algorithms have been proposed to do the above processes. Frequent pattern mining has been a focused topic in data mining research with a good number of references in literature and for that reason an important progress has been made, varying from performant algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining. Association Rule mining (ARM) is one of the utmost current data mining techniques designed to group objects together from large databases aiming to extract the interesting correlation and relation among huge amount of data. In this article, we provide a brief review and analysis of the current status of frequent pattern mining and discuss some promising research directions. Additionally, this paper includes a comparative study between the performance of the described approaches.Comment: 14 pages, 3 figures. arXiv admin note: text overlap with arXiv:1312.4800; and with arXiv:1109.2427 by other author

Evaluation of ADAM/1 model for advanced coal extraction concepts

Several existing computer programs for estimating life cycle cost of mining systems were evaluated. A commercially available program, ADAM/1 was found to be satisfactory in relation to the needs of the advanced coal extraction project. Two test cases were run to confirm the ability of the program to handle nonconventional mining equipment and procedures. The results were satisfactory. The model, therefore, is recommended to the project team for evaluation of their conceptual designs

Applications of concurrent access patterns in web usage mining

This paper builds on the original data mining and modelling research which has proposed the discovery of novel structural relation patterns, applying the approach in web usage mining. The focus of attention here is on concurrent access patterns (CAP), where an overarching framework illuminates the methodology for web access patterns post-processing. Data pre-processing, pattern discovery and patterns analysis all proceed in association with access patterns mining, CAP mining and CAP modelling. Pruning and selection of access pat-terns takes place as necessary, allowing further CAP mining and modelling to be pursued in the search for the most interesting concurrent access patterns. It is shown that higher level CAPs can be modelled in a way which brings greater structure to bear on the process of knowledge discovery. Experiments with real-world datasets highlight the applicability of the approach in web navigation

Relational Data Mining Through Extraction of Representative Exemplars

With the growing interest on Network Analysis, Relational Data Mining is becoming an emphasized domain of Data Mining. This paper addresses the problem of extracting representative elements from a relational dataset. After defining the notion of degree of representativeness, computed using the Borda aggregation procedure, we present the extraction of exemplars which are the representative elements of the dataset. We use these concepts to build a network on the dataset. We expose the main properties of these notions and we propose two typical applications of our framework. The first application consists in resuming and structuring a set of binary images and the second in mining co-authoring relation in a research team

Weaving Entities into Relations: From Page Retrieval to Relation Mining on the Web

With its sheer amount of information, the Web is clearly an important frontier for data mining. While Web mining must start with content on the Web, there is no effective ``search-based'' mechanism to help sifting through the information on the Web. Our goal is to provide a such online search-based facility for supporting query primitives, upon which Web mining applications can be built. As a first step, this paper aims at entity-relation discovery, or E-R discovery, as a useful function-- to weave scattered entities on the Web into coherent relations. To begin with, as our proposal, we formalize the concept of E-R discovery. Further, to realize E-R discovery, as our main thesis, we abstract tuple ranking-- the essential challenge of E-R discovery-- as pattern-based cooccurrence analysis. Finally, as our key insight, we observe that such relation mining shares the same core functions as traditional page-retrieval systems, which enables us to build the new E-R discovery upon today's search engines, almost for free. We report our system prototype and testbed, WISDM-ER, with real Web corpus. Our case studies have demonstrated a high promise, achieving 83%-91% accuracy for real benchmark queries-- and thus the real possibilities of enabling ad-hoc Web mining tasks with online E-R discovery

Collective Representations, Divided Memory and Patterns of Paradox: Mining and Shipbuilding

This paper seeks to examine the different relationship of two industries to their potential for representation and celebration in collective memory. Looking at case studies of mining and shipbuilding in the shared location of Wearside the paper compares and contrasts features of the two industries in relation to the divergent outcomes of the traces of their collective memory in this place. Using visual representations the paper makes the case that the mining industry has experienced a successful recovery of memory. This is contrasted to the paucity of visual representation in relation to shipbuilding. The reasons for the contrast in the viability of collective memory are examined. Material, cultural and aesthetic issues are addressed. Contrasts are drawn between divisions of labour in the two industries and the ways in which these impact upon community and trade union organisation which further relate to the contrast between industrial and occupational identity. Differences in the legacy of the physical occupational communities of the two industries are illustrated. There is also an examination of the aesthetic forms of representation in which mining is seen as characterised by the aesthetics of labour, whereas shipbuilding is represented more through the aesthetics of product. The way in which the industries were closed also becomes important to understand the variation in the differences of the potential of collective memory. All of these strands are brought together to conclude that in relation to the potential for collective memory, mining can be seen to have gone through a process of 'mourning' whereas melancholia seems to more adequately represent the situation with respect to shipbuilding. In illustrating these cases the paper is arguing for a more sophisticated understanding of the process of deindustrialisation and the potential for the recovery of collective memory.Collective Memory, Mourning, Melancholia, Deindustrialization, Post-Industrial Community, Locality, Mining, Shipbuilding

Strategic assessment of the magnitude and impacts of sand mining in Poyang Lake, China

Planning for the extraction of aggregates is typically dealt with at a case to case basis, without assessing environmental impacts strategically. In this study we assess the impact of sand mining in Poyang Lake, where dredging began in 2001 after sand mining in the Yangtze River had been banned. In April 2008 concern over the impact on the biodiversity led to a ban on sand mining in Poyang Lake until further plans could be developed. Planning will require consideration of both sand extraction in relation to available sediment resources and also environmental impacts within the context of future demand for sand in the lower Yangtze Valley. We used pairs of near-infrared (NIR) Aster satellite imagery to estimate the number of vessels leaving the lake. Based on this we calculated a rate of sand extraction of 236 million m3 year-1 in 2005–2006. This corresponds to 9% of the total Chinese demand for sand. It qualifies Poyang Lake as probably the largest sand mining operation in the world. It also indicates that sand extraction currently dominates the sediment balance of the lower Yangtze River. A positive relation between demand for sand and GDP, revealed by historic data from the USA, suggests that the current per capita demand for sand in China might increase in the near future from 2 to 4 m3 year-1. We review various environmental impacts and question whether it will be possible to preserve the rich biodiversity of the lake, while continuing at the same time satisfying the increasing Chinese demand for sand. Finally we review alternative options for sand mining, in order to relieve the pressure from the Poyang Lake ecosyste

Graph-based Modelling of Concurrent Sequential Patterns

Structural relation patterns have been introduced recently to extend the search for complex patterns often hidden behind large sequences of data. This has motivated a novel approach to sequential patterns post-processing and a corresponding data mining method was proposed for Concurrent Sequential Patterns (ConSP). This article refines the approach in the context of ConSP modelling, where a companion graph-based model is devised as an extension of previous work. Two new modelling methods are presented here together with a construction algorithm, to complete the transformation of concurrent sequential patterns to a ConSP-Graph representation. Customer orders data is used to demonstrate the effectiveness of ConSP mining while synthetic sample data highlights the strength of the modelling technique, illuminating the theories developed