Parallel rule induction with information theoretic pre-pruning

In a world where data is captured on a large scale the major challenge for data mining algorithms is to be able to scale up to large datasets. There are two main approaches to inducing classification rules, one is the divide and conquer approach, also known as the top down induction of decision trees; the other approach is called the separate and conquer approach. A considerable amount of work has been done on scaling up the divide and conquer approach. However, very little work has been conducted on scaling up the separate and conquer approach.In this work we describe a parallel framework that allows the parallelisation of a certain family of separate and conquer algorithms, the Prism family. Parallelisation helps the Prism family of algorithms to harvest additional computer resources in a network of computers in order to make the induction of classification rules scale better on large datasets. Our framework also incorporates a pre-pruning facility for parallel Prism algorithms.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Towards a computationally efficient approach to modular classification rule induction

Induction of classification rules is one of the most important technologies in data mining. Most of the work in this field has concentrated on the Top Down Induction of Decision Trees (TDIDT) approach. However, alternative approaches have been developed such as the Prism algorithm for inducing modular rules. Prism often produces qualitatively better rules than TDIDT but suffers from higher computational requirements. We investigate approaches that have been developed to minimize the computational requirements of TDIDT, in order to find analogous approaches that could reduce the computational requirements of Prism.

Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

A ferromagnetic methoxido-bridged Mn(III) dimer and a spin-canted metamagnetic μ1,3-azido-bridged chain

Two new Mn(III) complexes of formulas [MnL1(N-3)(OMe)](2) (1) and [MnL2(N-3)(2)](n) (2) have been synthesized by using two tridentate NNO-donor Schiff base ligands HL1{(2-[(3-methylaminoethylimino)-methyl]-phenol)} and HL2 {(2-[1-(2-dimethylaminoethylimino)methyl]-phenol)}, respectively. Substitution of the H atom on the secondary amine group of the N-methyldiamine fragment of the Schiff base by a methyl group leads to a drastic structural change from a methoxido-bridged dimer (1) to a single mu(1,3)-azido-bridged 1D helical polymer (2). Both complexes were characterized by single-crystal X-ray structural analyses and variable-temperature magnetic susceptibility measurements. The magnetic properties of compound I show the presence of weak ferromagnetic exchange interactions mediated by double methoiddo bridges (J = 0.95 cm(-1)). Compound 2 shows the existence of a weak antiferromangetic coupling along the chain (J = -8.5 cm(-1)) through the single mu(1,3)-N-3 bridge with a spin canting that leads to a long-range antiferromagnetic order at T-c approximate to 9.3 K and a canting leading to a weak ferromagnetic long-range order at T-c approximate to 8.5 K. It also exibits metamagnetic behavior at low temperatures with a critical field of ca.1.2 T due to the weak antiferromagnetic interchain interactions that appear in the canted ordered phase.

A rare case of solution and solid state inter-conversion of two copper(II) dimers and a copper(II) chain

Three Cu(II)-azido complexes of formula [Cu2L2(N-3)(2)] (1), [Cu2L2(N-3)(2)]center dot H2O (2) and [CuL(N-3)](n) (3) have been synthesized using the same tridentate Schiff base ligand HL (2-[(3-methylaminopropylimino)-methyl]-phenol), the condensation product of N-methyl-1,3-propanediamine and salicyldehyde). Compounds 1 and 2 are basal-apical mu-1,1 double azido bridged dimers. The dimeric structure of 1 is centro-symmetric but that of 2 is non-centrommetric. Compound 3 is a mu-1,1 single azido bridged 1D chain. The three complexes interconvert in solution and can be obtained in pure form by carefully controlling the synthetic conditions. Compound 2 undergoes an irreversible transformation to 1 upon dehydration in the solid state. The magnetic properties of compounds 1 and 2 show the presence of weak antiferromagnetic exchange interactions mediated by the double 1,1-N-3 azido bridges (J = -2.59(4) and -0.10(1) cm-(1), respectively). The single 1,1-N-3 bridge in compound 3 mediates a negligible exchange interaction.

Oppositely charged polyelectrolytes: complex formation and effects of chain asymmetry

The formation of complexes in solutions of oppositely charged polyions has been studied by Monte Carlo simulations. The amount as well as the length, and thus, the absolute charge of one of the polyions have been varied. There is an increasing tendency to form large clusters as the excess of one kind of polyion decreases. When all polyions have the same length, this tendency reaches a maximum near, but off, equivalent amounts of the two types of polyions. When one kind of polyion is made shorter, the propensity to form large clusters decreases and the fluctuations in cluster charge increases. Simple free-energy expressions have been formulated on the basis of a set of simple rules that help rationalize the observations. By calculating cluster distributions in both grand canonical and canonical ensembles, it has been possible to show the extent of finite-size effects in the simulations.

Mutual dependency grid for stakeholder mapping: a component-based approach to supply chain participant analysis

Stakeholder analysis plays a critical role in business analysis. However, the majority of the stakeholder identification and analysis methods focus on the activities and processes and ignore the artefacts being processed by human beings. By focusing on the outputs of the organisation, an artefact-centric view helps create a network of artefacts, and a component-based structure of the organisation and its supply chain participants. Since the relationship is based on the components, i.e. after the stakeholders are identified, the interdependency between stakeholders and the focal organisation can be measured. Each stakeholder is associated with two types of dependency, namely the stakeholder’s dependency on the focal organisation and the focal organisation’s dependency on the stakeholder. We identify three factors for each type of dependency and propose the equations that calculate the dependency indexes. Once both types of the dependency indexes are calculated, each stakeholder can be placed and categorised into one of the four groups, namely critical stakeholder, mutual benefits stakeholder, replaceable stakeholder, and easy care stakeholder. The mutual dependency grid and the dependency gap analysis, which further investigates the priority of each stakeholder by calculating the weighted dependency gap between the focal organisation and the stakeholder, subsequently help the focal organisation to better understand its stakeholders and manage its stakeholder relationships.

Analysis and compensation of I/Q imbalance in MIMO transmit-receive diversity systems

In wireless communication systems, all in-phase and quadrature-phase (I/Q) signal processing receivers face the problem of I/Q imbalance. In this paper, we investigate the effect of I/Q imbalance on the performance of multiple-input multiple-output (MIMO) maximal ratio combining (MRC) systems that perform the combining at the radio frequency (RF) level, thereby requiring only one RF chain. In order to perform the MIMO MRC, we propose a channel estimation algorithm that accounts for the I/Q imbalance. Moreover, a compensation algorithm for the I/Q imbalance in MIMO MRC systems is proposed, which first employs the least-squares (LS) rule to estimate the coefficients of the channel gain matrix, beamforming and combining weight vectors, and parameters of I/Q imbalance jointly, and then makes use of the received signal together with its conjugation to detect the transmitted signal. The performance of the MIMO MRC system under study is evaluated in terms of average symbol error probability (SEP), outage probability and ergodic capacity, which are derived considering transmission over Rayleigh fading channels. Numerical results are provided and show that the proposed compensation algorithm can efficiently mitigate the effect of I/Q imbalance.

An environmental uncertainty-based diagnostic reference tool for evaluating the performance of supply chain value streams

This research has responded to the need for diagnostic reference tools explicitly linking the influence of environmental uncertainty and performance within the supply chain. Uncertainty is a key factor influencing performance and an important measure of the operating environment. We develop and demonstrate a novel reference methodology based on data envelopment analysis (DEA) for examining the performance of value streams within the supply chain with specific reference to the level of environmental uncertainty they face. In this paper, using real industrial data, 20 product supply value streams within the European automotive industry sector are evaluated. Two are found to be efficient. The peer reference groups for the underperforming value streams are identified and numerical improvement targets are derived. The paper demonstrates how DEA can be used to guide supply chain improvement efforts through role-model identification and target setting, in a way that recognises the multiple dimensions/outcomes of the supply chain process and the influence of its environmental conditions. We have facilitated the contextualisation of environmental uncertainty and its incorporation into a specific diagnostic reference tool.