Previous Mining Research Articles

On pushing weight constraints deeply into frequent itemset mining

There have been many studies on mining frequent itemset (or pattern) in the data mining field because of its broad applications in mining association rules, correlations, graph patterns, constraint based frequent patterns, sequential patterns, and many other data mining tasks. One of major challenges in frequent pattern mining is a huge number of result patterns. As the minimum threshold becomes lower, an exponentially large number of itemsets are generated. Therefore, pruning unimportant patterns effectively in mining process is one of main topics in frequent pattern mining. In weighted frequent pattern mining, not only support but also weight are used and important patterns can be detected. In this paper, we propose two efficient algorithms for mining weighted frequent itemsets in which the main approaches are to push weight constraints into the Apriori algorithm and the pattern growth algorithm respectively. Additionally, we show how to maintain the downward closure property in mining weighted frequent itemsets. In our approach, the normalized weights within the weight range are used according to the importance of items. A weight range is used to restrict weights of items and a minimum weight is utilized to balance between weight and support of items for pruning the search space. Our approach generates fewer but important weighted frequent itemsets in large databases, particularly dense databases with low minimum supports. An extensive performance study shows that our algorithm outperforms previous mining algorithms. In addition, it is efficient and scalable.

On Identifying Useful Patterns to Analyze Products in Retail Transaction Databases

Mining correlated patterns in large transaction databases is one of the essential tasks in data mining since a huge number of patterns are usually mined, but it is hard to find patterns with the correlation. The needed data analysis should be made according to the requirements of the particular real application. In previous mining approaches, patterns with the weak affinity are found even with a high minimum support. In this paper, we suggest weighted support affinity pattern mining in which a new measure, weighted support confidence (ws-confidence) is developed to identify correlated patterns with the weighted support affinity. To efficiently prune the weak affinity patterns, we prove that the ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate patterns with dissimilar weighted support levels. Based on the two properties, we develop a weighted support affinity pattern mining algorithm (WSP). The weighted support affinity patterns can be useful to answer the comparative analysis queries such as finding itemsets containing items which give similar total selling expense levels with an acceptable error range α% and detecting item lists with similar levels of total profits. In addition, our performance study shows that WSP is efficient and scalable for mining weighted support affinity patterns.

Three Missouri Alfisols Impacted by Aeolian Deposition of Lead-Zinc-Cadmium-Silver-Indium Bearing Mine Tailings

Modern mining technologies promote the nation's gross domestic product and reduce environmental impacts; however, previous mining activities in Missouri have a legacy of soil contamination. This study focuses on Pb-Cd-Zn-Ag-In soil contamination resulting from a closed Pb-Zn mine at St. Joe State Park in Missouri. Metal contamination is the result of ancestral smelting operations and presently exposed tailings. Three soil profiles located adjacent to a closed mine-smelting operation and two reference pedons located outside of the study area were examined to determine the concentrations of Pb, Cd, Zn, Ag, and In. Aqua regia digestion and selective chemical extractions were employed to assess metal availability. The near-surface soil horizons show dramatically greater Pb, Cd, Zn, Ag, and In concentrations than deeper soil horizons or the reference loess-derived soil profiles. Surface Pb concentrations in the study area range from 224 to 589 mg Pb kg − 1 , whereas the surface Cd concentrations range from 0.3 to 3.1 mg Cd kg− 1. Pyrophosphate and EDTA (Ethylenediaminetetraacetic Acid) extractions demonstrate that a small to substantial portion of the total Pb and Cd pool is potentially available. Water soluble metal concentrations infer that the risk of metal leaching from the soil is minimal.

Effectiveness of Triple Superphosphate Amendments in Alleviating Soil Lead Accumulation in Missouri Alfisols

Modern mining technologies reduce the environmental impact; however, previous mining activities in Missouri have a legacy of soil contamination. This study focuses on resolving Pb-Cd-Zn soil contamination resulting from an abandoned Pb-Zn mine at St. Joe State Park in Missouri by amending the soil surface with triple superphosphate (TSP) to reduce the availability of lead. Surface Pb concentrations ranged from 224 to 589 mg Pb kg − 1 and the surface Cd concentrations ranged from 0.3 to 3.1 mg Cd kg − 1 . Two soils were amended with three rates of TSP for each soil series. Soils were evaluated using selective extractions and a biological assay involving soybean (Glycine max Merr.) cultured in the greenhouse, with the biomass and the Pb and Cd plant tissue concentrations determined. In general, soil exchangeable Pb concentrations and the Pb plant tissue concentrations did not support the premise that TSP treatment reduced the Pb biological availability. For the Caneyville series (Typic Hapludalfs) the TSP reacted preferentially with Ca, limiting the Pb alteration and reducing the effectiveness of TSP treatments on these Ca rich soils. For the more acidic Hildebrecht series (Oxyaquic Fragiudalfs), the TSP treatments did not reduce plant accumulation of Pb, presumably because sufficient soil Ca was present.

Covering Pre-existing, Acid-producing Fills with Alkaline Sandstone to Control Acid Mine Drainage

In Boone County, West Virginia, small valley fills composed of overburden from the Number 5 Block coal seam were constructed during previous mining operations before overburden analysis was required to predict acid-producing potential. Using the modified acid–base accounting procedure, we found that these fills contain acid-producing materials, and in fact, they do produce acidic drainage. Black Castle Mining Company began operating in the area during the late 1980s and, in the process of removing coal from deeper in the geologic column, encountered alkaline sandstone, which they used to construct valley fills around and below the pre-existing fills that were discharging acidic drainage. Water quality from existing fills had pH from 3.5 to 4.5, and acidities up to 200 mg/L as CaCO3. After covering the existing fills with alkaline sandstone, drainage water had a pH of >6.2 and contained net alkalinity. This has eliminated the need for chemical treatment of acid mine drainage from the pre-existing, pre-law valley fills.

Regularity and prediction of ground pressure in Haigou Gold Mine

Previous mining excavation in upper sublevels left several mined-out areas in Haigou gold mine. To ensure safety of the main and auxiliary shafts and mining production in deeper sublevels, systematical studies on regularity, prediction, and control of ground pressure in the mine were carried out. Through 3D-numerical modeling and in-situ monitoring of acoustic emission, pressure and displacement, the ground pressure activity and the stability status of surrounding rock masses and the two shafts were assessed. Based on in-situ monitoring practice in Haigou mine, 4 modes to judge rock stability according to the monitoring information of acoustic emission, pressure, and displacement were presented.

Bottom-up discovery of frequent rooted unordered subtrees

In the past decade, XML has emerged as the standard language for information exchanging over the Internet. Due to its tree-structure paradigm, XML is superior for its capability of storing, querying, and manipulating complex data. Therefore, discovering frequent tree patterns over tree-structured data has become an interesting topic for XML data management. In this paper, we propose a tree mining algorithm, named BUXMiner, for finding a special class of frequent trees, called rooted unordered trees, from a tree-structured database. BUXMiner employs an efficient bottom-up approach to enumerate all candidate trees over a compact global tree guide and computes the frequent trees based on the tree guide. In addition to BUXMiner, we also propose a mining approach called BUMXMiner to discover the maximal frequent rooted unordered trees. We compare BUXMiner with previous tree-structure mining algorithms, namely XQPMinerTID and FastXMiner, which were also proposed to discover rooted unordered trees. The experimental results show that our algorithm outperforms XQPMinerTID and FastXMiner in terms of efficiency. The performance results from real-world applications also indicate the usefulness of our proposed tree mining algorithms in a variety of web applications, such as analysis of web page access patterns and mining frequent XML query patterns for caching.

Impact Assessment and Remediation Strategies for Roadway Construction in Acid-Bearing Media: Case Study from Mid-Appalachia

The likelihood of encountering land impacted by current and/or historic coal mining activities is high when constructing roadways in the Mid-Appalachian region. Through additional disturbance of these lands, environmental impacts such as acid and dissolved metals loading and subsequent impacts to aquatic flora and fauna will ensue. Consequently, it is necessary to affect a paradigm shift in roadway design and construction to account for the presence of factors that compound the already difficult task of working in a region characterized by steep topography and aggressive geochemistry. In this study, assessments of the water chemistry and biological impacts of a waste pile containing spoils from previous mining and the presence of an exposed coal mine bench were made as representative microcosmic examples of typical conditions found in the region. Based on quantitative measurements of water quality and biological conditions, recommendations are presented for the assessment and avoidance of impacts prior to construction through acid-bearing materials and suggestions are offered for postconstruction remediation at previously impacted sites.

Mining fuzzy association rules from questionnaire data

Association rule mining is one of most popular data analysis methods that can discover associations within data. Association rule mining algorithms have been applied to various datasets, due to their practical usefulness. Little attention has been paid, however, on how to apply the association mining techniques to analyze questionnaire data. Therefore, this paper first identifies the various data types that may appear in a questionnaire. Then, we introduce the questionnaire data mining problem and define the rule patterns that can be mined from questionnaire data. A unified approach is developed based on fuzzy techniques so that all different data types can be handled in a uniform manner. After that, an algorithm is developed to discover fuzzy association rules from the questionnaire dataset. Finally, we evaluate the performance of the proposed algorithm, and the results indicate that our method is capable of finding interesting association rules that would have never been found by previous mining algorithms.

Bottom-up mining of XML query patterns to improve XML querying

Querying XML data is a computationally expensive process due to the complex nature of both the XML data and the XML queries. In this paper we propose an approach to expedite XML query processing by caching the results of frequent queries. We discover frequent query patterns from user-issued queries using an efficient bottom-up mining approach called VBUXMiner. VBUXMiner consists of two main steps. First, all queries are merged into a summary structure named “compressed global tree guide” (CGTG). Second, a bottom-up traversal scheme based on the CGTG is employed to generate frequent query patterns. We use the frequent query patterns in a cache mechanism to improve the XML query performance. Experimental results show that our proposed mining approach outperforms the previous mining algorithms for XML queries, such as XQPMiner TID and FastXMiner, and that by caching the results of frequent query patterns, XML query performance can be dramatically improved.

An efficient mining of weighted frequent patterns with length decreasing support constraints

Frequent pattern mining has been extensively studied in the data mining field due to its broad applications in mining association rules, correlations, closed frequent patterns, graph patterns, constraint-based frequent patterns, sequential patterns, and many other data mining tasks. Two concerns exist for frequent pattern mining in the real world. First, each item has different importance so researchers have proposed weighted frequent pattern mining algorithms that reflect the importance of items. Second, previous mining algorithms use a constant support constraint irrespective of the length of discovered patterns. However, short patterns having only a fewer items tend to be interesting if they have high support, while long patterns can still be interesting although their supports are relatively low. Weight and length decreasing support constraints are important constraints, but no mining algorithms consider both constraints. In this paper, we propose weighted frequent pattern mining with length decreasing support constraints. Our main approach is to push weight constraints and length decreasing support constraints into the pattern growth algorithm. For pruning techniques, we propose the notion of the Weighted Smallest Valid Extension (WSVE) property with/without Minimum Weight (MinW). The WSVE property with/without MinW is applied to transaction pruning, node pruning and path pruning to eliminate weighted infrequent patterns earlier. Our approach generates more concise but important weighted frequent patterns with length decreasing support constraints by applying the WSVE property with/without MinW.

Mining association rules from imprecise ordinal data

Categorical data can generally be classified into ordinal data and nominal data. Although there have been numerous studies on finding association rules from nominal data, few have tried to do so from ordinal data. Additionally, previous mining algorithms usually assume that the input data is precise and clean, which is unrealistic in practical situations. Real-world data tends to be imprecise due to human errors, instrument errors, recording errors, and so on. Therefore, this paper proposes a new approach to discovering association rules from imprecise ordinal data. Experimental results from the survey data show the feasibility of the proposed mining algorithm. Performance analyses of the algorithms also show that the proposed approach can discover interesting and valuable rules that could never be found using the conventional approach, the Apriori algorithm.

Incremental and interactive mining of web traversal patterns

Web mining involves the application of data mining techniques to large amounts of web-related data in order to improve web services. Web traversal pattern mining involves discovering users’ access patterns from web server access logs. This information can provide navigation suggestions for web users indicating appropriate actions that can be taken. However, web logs keep growing continuously, and some web logs may become out of date over time. The users’ behaviors may change as web logs are updated, or when the web site structure is changed. Additionally, it can be difficult to determine a perfect minimum support threshold during the data mining process to find interesting rules. Accordingly, we must constantly adjust the minimum support threshold until satisfactory data mining results can be found. The essence of incremental data mining and interactive data mining is the ability to use previous mining results in order to reduce unnecessary processes when web logs or web site structures are updated, or when the minimum support is changed. In this paper, we propose efficient incremental and interactive data mining algorithms to discover web traversal patterns that match users’ requirements. The experimental results show that our algorithms are more efficient than other comparable approaches.

Mesophilic Actinomycetes in the Natural and Reconstructed Sand Dune Vegetation Zones of Fraser Island, Australia

The natural coastal habitat of Fraser Island located in the State of Queensland, Australia, has been disturbed in the past for mining of the mineral sand ilmenite. Currently, there is no information available on whether these past mining disturbances have affected the distribution, diversity, and survival of beneficial soil microorganisms in the sand dunes of the island. This in turn could deleteriously affect the success of the natural regeneration, plant growth, and establishment on the sand dunes. To support ongoing restoration efforts at sites like these mesophilic actinomycetes were isolated using conventional techniques, with particular emphasis on the taxa previously reported to produce plant-growth-promoting substances and providing support to mycorrhizal fungi, were studied at disturbed sites and compared with natural sites. In the natural sites, foredunes contained higher densities of micromonosporae replaced by increasing numbers of streptomycete species in the successional dune and finally leading to complex actinomycete communities in the mature hind dunes. Whereas in the disturbed zones affected by previous mining activities, which are currently being rehabilitated, no culturable actinomycete communities were detected. These findings suggest that the paucity of beneficial microflora in the rehabilitated sand dunes may be limiting the successful colonization by pioneer plant species. Failure to establish a cover of plant species would result in the mature hind dune plants being exposed to harsh salt and climatic conditions. This could exacerbate the incidence of wind erosion, resulting in the destabilization of well-defined and vegetated successional dunal zones.

Analyzing Sequential Patterns in Retail Databases

Finding correlated sequential patterns in large sequence databases is one of the essential tasks in data mining since a huge number of sequential patterns are usually mined, but it is hard to find sequential patterns with the correlation. According to the requirement of real applications, the needed data analysis should be different. In previous mining approaches, after mining the sequential patterns, sequential patterns with the weak affinity are found even with a high minimum support. In this paper, a new framework is suggested for mining weighted support affinity patterns in which an objective measure, sequential ws-confidence is developed to detect correlated sequential patterns with weighted support affinity patterns. To efficiently prune the weak affinity patterns, it is proved that ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate sequential patterns with dissimilar weighted support levels. Based on the framework, a weighted support affinity pattern mining algorithm (WSMiner) is suggested. The performance study shows that WSMiner is efficient and scalable for mining weighted support affinity patterns.

빈발 유전자 발현 패턴과 연쇄 규칙을 이용한 유전자 조절 네트워크 구축

Groups of genes control the functioning of a cell by complex interactions. Such interactions of gene groups are tailed Gene Regulatory Networks(GRNs). Two previous data mining approaches, clustering and classification, have been used to analyze gene expression data. Though these mining tools are useful for determining membership of genes by homology, they don`t identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rules. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and gene expression patterns we detected by applying the FP-growth algorithm. Next, we construct a gene regulatory network from frequent gene patterns using chain rules. Finally, we validate our proposed method through our experimental results, which are consistent with published results.

Efficient algorithms for mining maximal valid groups

A valid group is defined as a group of moving users that are within a distance threshold from one another for at least a minimum time duration. Unlike grouping of users determined by traditional clustering algorithms, members of a valid group are expected to stay close to one another during their movement. Each valid group suggests some social grouping that can be used in targeted marketing and social network analysis. The existing valid group mining algorithms are designed to mine a complete set of valid groups from time series of user location data, known as the user movement database. Unfortunately, there are considerable redundancy in the complete set of valid groups. In this paper, we therefore address this problem of mining the set of maximal valid groups. We first extend our previous valid group mining algorithms to mine maximal valid groups, leading to AMG and VGMax algorithms. We further propose the VGBK algorithm based on maximal clique enumeration to mine the maximal valid groups. The performance results of these algorithms under different sets of mining parameters are also reported.

Mining Frequent Fuzzy Grids in Dynamic Databases with Weighted Transactions and Weighted Items

Incremental mining algorithms that derive the latest mining output by making use of previous mining results are attractive to business organisations. In this paper, a fuzzy data mining algorithm for incremental mining of frequent fuzzy grids from quantitative dynamic databases is proposed. It extends the traditional association rule problem by allowing a weight to be associated with each item in a transaction and with each transaction in a database to reflect the interest/intensity of items and transactions. It uses the information about fuzzy grids that are already mined from original database and avoids start-from-scratch process. In addition, we deal with "weights-of-significance" which are automatically regulated as the incremental databases are evolved and implant themselves in the original database. We maintain "hopeful fuzzy grids" and "frequent fuzzy grids" and our algorithm changes the status of the grids which have been discovered earlier so that they reflect the pattern drift in the updated quantitative databases. Our heuristic approach avoids maintaining many "hopeful fuzzy grids" at the initial level. The algorithm is illustrated with one numerical example and demonstration of experimental results are also incorporated.

A GIS-based Weights-of-Evidence model for mapping cliff instabilities associated with mine subsidence

The Weights-of-Evidence (W-of-E) technique was applied, within a geographic information system (GIS), to derive a model of rockfall potential associated with mining-induced subsidence. The purpose of the model was to describe the potential for rockfalls from up to 60 m high steep sandstone gorges and slopes associated with proposed underground longwall operations within the immediate vicinity of a previously mined area. Ten known rock falls associated with the previous mining operation were used as training points. Six evidential themes were considered-slope, cliff height, planform curvature, profile curvature, the distance of the cliff areas from the longwall panels, and the distance of the cliff areas from the river. Two models were created, one based on a mine layout in which longwall panels extend beneath the steep areas of a nearby river, and a second in which the mine layout is modified so that mining does not occur directly beneath or within 50 m of the steep slopes. This is to allow for the comparison of rockfall potential based on different mining configurations. The results demonstrate that the W-of-E method is a suitable tool for mine subsidence impact assessment, and suggest that not mining directly under the Nepean river may decrease rockfall potential, on average, by approximately ten times. Numerous limitations with the results, relating to the availability of appropriate evidential theme data and the accuracy of training points, are discussed.

A New Incremental Updating Algorithm for Mining Sequential Patterns

This study discusses how to maintain discovered sequential patterns when some information is deleted from a sequence database. A new algorithm, called MA_D (Maintenance Algorithm when Deleting some information), is presented in order to deal with the maintenance of sequential patterns mining resulted from the updating of database and the algorithm makes full use of the information obtained from previous mining results to cut down the cost of finding new sequential patterns in an updated database. Our experimental analysis shows that the new algorithm is more efficient.