Frequent Data Mining Research Articles

A COMPARATIVE ANALYSIS OF K-MEANS AND HIERARCHICAL CLUSTERING

Clustering is the process of arranging comparable data elements into groups. One of the most frequent data mining analytical techniques is clustering analysis; the clustering algorithm’s strategy has a direct influence on the clustering results. This study examines the many types of algorithms, such as k-means clustering algorithms, and compares and contrasts their advantages and disadvantages. This paper also highlights concerns with clustering algorithms, such as time complexity and accuracy, in order to give better outcomes in a variety of environments. The outcomes are described in terms of big datasets. The focus of this study is on clustering algorithms with the WEKA data mining tool. Clustering is the process of dividing a big data set into small groups or clusters. Clustering is an unsupervised approach that may be used to analyze big datasets with many characteristics. It’s a data-modeling technique that provides a clear image of your data. Two clustering methods, k-means and hierarchical clustering, are explained in this survey and their analysis using WEKA tool on different data sets. KEYWORDS: data clustering, weka , k-means, hierarchical clustering

An Overview of Data Mining and Process Mining Applications in Underground Mining

The underground mining process can be analysed with a data-oriented or process-oriented approach. The first of them is popularand wide known as data mining while the second is still not often used in the conditions of the mining companies. The aim of thispaper is an overview of data mining and process mining applications in an underground mining domain and an investigation ofthe most popular analytic techniques used in the defined analytic perspectives (“Diagnostics and machinery”, “Geomechanics”,“Hazards”, “Mine planning and safety”). In the paper two research questions are formulated: RQ1: What are the most populardata mining/process mining tasks in the analysis of the underground mining process? and RQ2: What are the most popular datamining/process mining techniques applied in the multi-perspective analysis of the underground mining process? In the paper sixty--two published articles regarding to data mining tasks and analytic techniques in the mentioned domain have been analysed. Theresults show that predominatingly predictive tasks were formulated with regard to the analysed phenomena, with strong overrepresentationof classification task. The most frequent data mining algorithms is comprised of the following: artificial neural networks,decision trees, rule induction and regression. Only a few applications of process mining in analysis of the underground miningprocess have been found – they were briefly described in the paper.

TID based data and task parallelism for frequent data mining

Data mining is used to extract hidden information from a large set of data. From the various data mining algorithms available, frequent data mining is one of the most relevant algorithms. This algorithm has been used in varied fields ranging from medicine, weather forecasting, education, insurance, etc. Frequent mining (FM) algorithms have evolved from single processor to multiple processors. Limitation of using multiprocessors is increased cost and decreased throughput. Also the FM algorithms have been based on single process. This is an impediment in case of large data. One solution is to use multiple processes in multi-core environment.In this paper we propose a FM algorithm based on task parallelism. The algorithm is compared with FM algorithm based on data parallelism and serial algorithm.

Research on the Parallel Frequent Data Mining Strategy under the Cloud Computing Environment

Cloud computing is computing tasks distribution resources of a large number of computers in the subnet, to provide users with cheap and efficient computing power, storage capacity and service capabilities. Data mining is to find useful information in large data repository. Frequent flow of large amounts of data quickly and accurately find important basis for forecasting and decision, therefore, under the cloud computing environment parallelization frequent item data mining strategy to provide efficient solutions to store and analyze vast amounts of data has important theoretical significanceand application value.

Towards test suite reduction using maximal frequent data mining concept

All the industries are going through a revolution where software has become an essential component. Software testing is important to produce more reliable systems. Over the years this testing activity has evolved into a major resource consumer and cost driver for the software industries. This invariably leads to the addressing of key issues for efficient test case selection and managing test suite size. In this paper, a data mining-based algorithm has been proposed to address the issue of test suite optimisation. This algorithm focuses on selecting maximal frequent test sets for dataflow testing with Define Use DU pairs as requirements. Experimentation has been carried out to compare the relative performance and effectiveness of the proposed test suite reduction algorithm with the state-of-the-art algorithms: Harrold Gupta and Soffa HGS and Bi-Objective Greedy BOG using test metrics.

A DOMAIN-INDEPENDENT, TRANSFERABLE AND TIMELY ANALYSIS APPROACH TO ASSESS STUDENT COLLABORATION

Collaborative learning environments require intensive, regular and frequent analysis of the increasing amount of interaction data generated by students to assess that collaborative learning takes place. To support timely assessments that may benefit students and teachers the method of analysis must provide meaningful evaluations while the interactions take place. This research proposes machine learning-based techniques to infer the relationship between student collaboration and some quantitative domain-independent statistical indicators derived from large-scale evaluation analysis of student interactions. This paper (i) compares a set of metrics to identify the most suitable to assess student collaboration, (ii) reports on student evaluations of the metacognitive tools that display collaboration assessments from a new collaborative learning experience and (iii) extends previous findings to clarify modeling and usage issues. The advantages of the approach are: (1) it is based on domain-independent and generally observable features, (2) it provides regular and frequent data mining analysis with minimal teacher or student intervention, thereby supporting metacognition for the learners and corrective actions for the teachers, and (3) it can be easily transferred to other e-learning environments and include transferability features that are intended to facilitate its usage in other collaborative and social learning tools.

A data mining approach to evolutionary optimisation of noisy multi-objective problems

Many real world optimisation problems have opposing objective functions which are subjected to the influence of noise. Noise in the objective functions can adversely affect the stability, performance and convergence of evolutionary optimisers. This article proposes a Bayesian frequent data mining (DM) approach to identify optimal regions to guide the population amidst the presence of noise. The aggregated information provided by all the solutions helped to average out the effects of noise. This article proposes a DM crossover operator to make use of the rules mined. After implementation of this operator, a better convergence to the true Pareto front is achieved at the expense of the diversity of the solution. Consequently, an ExtremalExploration operator will be proposed in the later part of this article to help curb the loss in diversity caused by the DM operator. The result is a more directive search with a faster convergence rate. The search is effective in decision space where the Pareto set is in a tight cluster. A further investigation of the performance of the proposed algorithm in noisy and noiseless environment will also be studied with respect to non-convexity, discontinuity, multi-modality and uniformity. The proposed algorithm is evaluated on ZDT and other benchmarks problems. The results of the simulations indicate that the proposed method is effective in handling noise and is competitive against the other noise tolerant algorithms.