Vertical Mining Research Articles

Influence of temperature on accuracy of height connection measurement

AbstractThe purpose of depth measurement is to bring the altitude from the surface to the connected horizon (level) by means of a vertical mine. Points, for which the height is determined in this way, are part of the fundamental mining vertical control, and their height should therefore be determined as accurately as possible. There are several ways of this measurement and the temperature has influence on accuracy of each of them. The paper is dedicated to evaluation of influence of temperature, applying results of height connection measurement carried out at Karvina mine. Two measurements were performed in CSA 2 shaft and the other in shaft Mir 5 of Darkov mine in Ostrava-Karvina Coal District, CZE. Height connection measurement by a vertical mining work was done in two ways: using the depth tape and using the electro-optical distance meter.

Effective algorithms for vertical mining probabilistic frequent patterns in uncertain mobile environments

Data uncertainty is inherent in mobile applications. The traditional methods of mining frequent patterns are confronted with enormous challenges in uncertain mobile environments. The present achievements have shown that vertical mining algorithms are promising in mining expected support-based frequent patterns from uncertain data, while they have not captured much attention in mining probabilistic frequent patterns. In this paper, we propose two vertical mining algorithms UBEclat and NDUEclat for probabilistic frequent patterns mining FPM. The UBEclat algorithm is applied to discover exact probabilistic frequent patterns in uncertain environments, while the NDUEclat algorithm is suitable for mining probabilistic frequent patterns approximately in mobile environments with huge uncertain data. We test the two algorithms on real and synthetic datasets, and compare them with well-known FPM algorithms. The extensive evaluations show that the novel Eclat-based algorithms outperform the comparative ones in performance of efficiency and precision.

Effective algorithms for vertical mining probabilistic frequent patterns in uncertain mobile environments

Effective algorithms for vertical mining probabilistic frequent patterns in uncertain mobile environments

Fast Vertical Mining Using Boolean Algebra

The vertical association rules mining algorithm is an efficient mining method, which makes use of support sets of frequent itemsets to calculate the support of candidate itemsets. It overcomes the disadvantage of scanning database many times like Apriori algorithm. In vertical mining, frequent itemsets can be represented as a set of bit vectors in memory, which enables for fast computation. The sizes of bit vectors for itemsets are the main space expense of the algorithm that restricts its expansibility. Therefore, in this paper, a proposed algorithm that compresses the bit vectors of frequent itemsets will be presented. The new bit vector schema presented here depends on Boolean algebra rules to compute the intersection of two compressed bit vectors without making any costly decompression operation. The experimental results show that the proposed algorithm, Vertical Boolean Mining (VBM) algorithm is better than both Apriori algorithm and the classical vertical association rule mining algorithm in the mining time and the memory usage.

Mining Frequent Items in Uncertain Dataset

Because of uncertainty data, traditional algorithm of mining frequent items in certain dataset is difficult to apply to uncertain dataset. Considering characteristics of uncertain data, an improved vertical mining algorithm to find frequent items in uncertain dataset was proposed with the algorithm thought of classic vertical algorithm-Eclat in certain dataset. The improved algorithm merged TID field and corresponding probability field into probability vector. During the expansion of itemset and probability vector, itemset tree was established, and the support of candidate itemsets was calculated by means of vector operations. The improved algorithm is proved to be feasible and efficient according to experimental comparison and analysis.

Mixed-Integer Linear Programming formulation for block-cave sequence optimisation

Relying only on manual planning methods or computer software based on heuristic algorithms will lead to mine schedules that are not the optimal global solution. The objective of this paper is to develop a practical optimisation framework to schedule production for caving operations. We present two Mixed–Integer Linear Programming (MILP) formulations for the long–term production scheduling of block caving. First, we solve the problem at the drawpoint level. Then, we aggregate drawpoints into larger units referred to as clusters. The formulations are developed, implemented and verified in the TOMLAB/CPLEX environment. The production scheduler aims to maximise the Net Present Value (NPV) of the mining operation while the mine planner has control over the development rate, vertical mining rate, lateral mining rate, mining capacity, maximum number of active drawpoints and advancement direction. Application and comparison of the models for production scheduling using real mine data over 15 periods are presented.

A New Fast Vertical Method for Mining Frequent Patterns

Vertical mining methods are very effective for mining frequent patterns and usually outperform horizontal mining methods. However, the vertical methods become ineffective since the intersection time starts to be costly when the cardinality of tidset (tid-list or diffset) is very large or there are a very large number of transactions. In this paper, we propose a novel vertical algorithm called PPV for fast frequent pattern discovery. PPV works based on a data structure called Node-lists, which is obtained from a coding prefix-tree called PPC-tree. The efficiency of PPV is achieved with three techniques. First, the Node-list is much more compact compared with previous proposed vertical structure (such as tid-lists or diffsets) since transactions with common prefixes share the same nodes of the PPC-tree. Second, the counting of support is transformed into the intersection of Node-lists and the complexity of intersecting two Node-lists can be reduced to O(m+n) by an efficient strategy, where m and n are the cardinalities of the two Node-lists respectively. Third, the ancestor-descendant relationship of two nodes, which is the basic step of intersecting Node-lists, can be very efficiently verified by Pre-Post codes of nodes. We experimentally compare our algorithm with FP-growth, and two prominent vertical algorithms (Eclat and dEclat) on a number of databases. The experimental results show that PPV is an efficient algorithm that outperforms FP-growth, Eclat, and dEclat.

Vertical Mining of Frequent Patterns from Uncertain Data

Efficient algorithms have been developed for mining frequent patterns in traditional data where the content of each transaction is definitely known. There are many applications that deal with real data sets where the contents of the transactions are uncertain. Limited research work has been dedicated for mining frequent patterns from uncertain data. This is done by extending the state of art horizontal algorithms proposed for mining precise data to be suitable with the uncertainty environment. Vertical mining is a promising approach that is experimentally proved to be more efficient than the horizontal mining. In this paper we extend the state-of-art vertical mining algorithm Eclat for mining frequent patterns from uncertain data producing the proposed UEclat algorithm. In addition, we compared the proposed UEclat algorithm with the UF-growth algorithm. Our experimental results show that the proposed algorithm outperforms the UF-growth algorithm by at least one order of magnitude.

A New Fast Vertical Method for Mining Frequent Patterns

Vertical mining methods are very effective for mining frequent patterns and usually outperform horizontal mining methods. However, the vertical methods become ineffective since the intersection time starts to be costly when the cardinality of tidset (tid-list or diffset) is very large or there are a very large number of transactions. In this paper, we propose a novel vertical algorithm called PPV for fast frequent pattern discovery. PPV works based on a data structure called Node-lists, which is obtained from a coding prefix-tree called PPC-tree. The efficiency of PPV is achieved with three techniques. First, the Node-list is much more compact compared with previous proposed vertical structure (such as tid-lists or diffsets) since transactions with common prefixes share the same nodes of the PPC-tree. Second, the counting of support is transformed into the intersection of Node-lists and the complexity of intersecting two Node-lists can be reduced to O(m+n) by an efficient strategy, where m and n are the c...

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.

Canonical forms for labelled trees and their applications in frequent subtree mining

Tree structures are used extensively in domains such as computational biology, pattern recognition, XML databases, computer networks, and so on. In this paper, we first present two canonical forms for labelled rooted unordered trees–the breadth-first canonical form (BFCF) and the depth-first canonical form (DFCF). Then the canonical forms are applied to the frequent subtree mining problem. Based on the BFCF, we develop a vertical mining algorithm, RootedTreeMiner, to discover all frequently occurring subtrees in a database of labelled rooted unordered trees. The RootedTreeMiner algorithm uses an enumeration tree to enumerate all (frequent) labelled rooted unordered subtrees. Next, we extend the definition of the DFCF to labelled free trees and present an Apriori-like algorithm, FreeTreeMiner, to discover all frequently occurring subtrees in a database of labelled free trees. Finally, we study the performance and the scalability of our algorithms through extensive experiments based on both synthetic data and datasets from real applications.

Turbo-charging vertical mining of large databases

In a vertical representation of a market-basket database, each item is associated with a column of values representing the transactions in which it is present. The association-rule mining algorithms that have been recently proposed for this representation show performance improvements over their classical horizontal counterparts, but are either efficient only for certain database sizes, or assume particular characteristics of the database contents, or are applicable only to specific kinds of database schemas. We present here a new vertical mining algorithm called VIPER, which is general-purpose, making no special requirements of the underlying database. VIPER stores data in compressed bit-vectors called “snakes” and integrates a number of novel optimizations for efficient snake generation, intersection, counting and storage. We analyze the performance of VIPER for a range of synthetic database workloads. Our experimental results indicate significant performance gains, especially for large databases, over previously proposed vertical and horizontal mining algorithms. In fact, there are even workload regions where VIPER outperforms an optimal, but practically infeasible, horizontal mining algorithm.

Numerical results for the nonaxisymmetric problem of the stability of vertical mining excavations

Numerical results for the nonaxisymmetric problem of the stability of vertical mining excavations

Problems of stability of vertical minings

1. The formulation of the problem of stability of vertical mining has been investigated here within the framework of the three-dimensional linearized theory of stability for small subcritical deformations, using the simplest model of rocks, i. e. a linear-elastic isotropic body. 2. Variational methods are developed for solving the problems and the solution is obtained for a mine of circular cross section with axisymmetric form of stability loss. 3. The asymptotic value of the critical load is computed for an arbitrary number of coordinate functions. 4. Using numerical methods and a computer sufficiently accurate value of critical loads is determined taking recourse to the three-dimensional linearized theory. The results are compared with those obtained by approximate methods.