A review of associative classification mining

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

The LSST Data Mining Research Agenda

We describe features of the LSST science database that are amenable to scientific data mining, object classification, outlier identification, anomaly detection, image quality assurance, and survey science validation. The data mining research agenda includes: scalability (at petabytes scales) of existing machine learning and data mining algorithms; development of grid-enabled parallel data mining algorithms; designing a robust system for brokering classifications from the LSST event pipeline (which may produce 10,000 or more event alerts per night); multi-resolution methods for exploration of petascale databases; indexing of multi-attribute multi-dimensional astronomical databases (beyond spatial indexing) for rapid querying of petabyte databases; and more.Comment: 5 pages, Presented at the "Classification and Discovery in Large Astronomical Surveys" meeting, Ringberg Castle, 14-17 October, 200

Evaluation and optimization of frequent association rule based classification

Deriving useful and interesting rules from a data mining system is an essential and important task. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task

Clinical data mining and classification

Dissertação para obtenção do Grau de Mestre em Engenharia Informática e de ComputadoresDeterminar os genes que contribuem para o desenvolvimento de certas doenças, como o cancro, é um objectivo importante na vanguarda da investigação clínica de hoje. Isto pode fornecer conhecimentos sobre como as doenças se desenvolvem, pode levar a novos tratamentos e a testes de diagnóstico que detectam doenças mais cedo no seu desenvolvimento, aumentando as hipóteses de recuperação dos pacientes. Hoje em dia, muitos conjuntos de dados de expressão genética estão disponíveis publicamente. Estes consistem geralmente em dados de microarray com informação sobre a activação (ou não) de milhares de genes, em pacientes específicos, que exibem uma determinada doença. No entanto, estes conjuntos de dados clínicos consistem em vetores de características de elevada dimensionalidade, o que levanta dificuldades à análise humana clínica e à interpretabilidade - dadas as grandes quantidades de características e as quantidades comparativamente pequenas de instâncias, é difícil identificar os genes mais relevantes relacionados com a presença de uma determinada doença. Nesta tese, exploramos a utilização da discretização de características, selecção de características e técnicas de classificação aplicadas ao problema de identificação do conjunto mais relevante de características (genes), dentro de conjuntos de dados de microarray, que podem prever a presença de uma dada doença. Construímos um pipeline onde aplicamos diferentes técnicas de discretização, selecção e classificação, a diferentes conjuntos de dados, e comparamos/interpretamos os resultados obtidos com cada combinação de técnicas. Na maioria dos conjuntos de dados, conseguimos obter erros de classificação mais baixos aplicando quer técnicas de discretização quer técnicas de selecção (mas não ambas). Ao aplicar técnicas de selecção, conseguimos também reduzir o número de características alimentadas a cada classificador, mantendo ou melhorando os resultados da classificação. Estes pequenos subconjuntos de genes são assim mais fáceis de interpretar pelos especialistas clínicos humanos, melhorando a explicabilidade dos resultados.Determining which genes contribute to the development of certain diseases, such as cancer, is an important goal in the forefront of today’s clinical research. This can provide insights on how diseases develop, can lead to new treatments and to diagnostic tests that detect diseases earlier in their development, increasing patients chances of recovery. Today, many gene expression datasets are publicly available. These generally consist of DNA microarray data with information on the activation (or not) of thousands of genes, in specific patients, that exhibit a certain disease. However, these clinical datasets consist of high-dimensional feature vectors, which raises difficulties for clinical human analysis and interpretability - given the large amounts of features and the comparatively small amounts of instances, it is difficult to identify the most relevant genes related to the presence of a particular disease. In this thesis, we explore the usage of feature discretization, feature selection, and classification techniques applied towards the problem of identifying the most relevant set of features (genes), within DNA microarray datasets, that can predict the presence of a given disease. We propose a machine learning pipeline with different feature discretization, feature selection, and classification techniques, to different datasets, and compare/interpret the achieved results with different combinations of techniques. On most datasets, we were able to obtain lower classification errors by applying either feature discretization or feature selection techniques (but not both). When applying feature selection techniques, we were also able to reduce the number of features fed to each classifier, while maintaining or improving the classification results. These smaller subsets of genes are thus easier to interpret by human clinical experts, improving the explainability of the results.N/