A Survey on Methods and Applications of Intelligent Market Basket Analysis Based on Association Rule

The market trends rapidly changed over the last two decades.The primary reason is the newly created opportunities and the increased number of competitors competing to grasp market share using business analysis techniques.Market Basket Analysis has a tangible effect in facilitating current change in the market.Market Basket Analysis is one of the famous fields that deal with Big Data and Data Mining applications.MBA initially uses Association Rule Learning(ARL)as a mean for realization.ARL has a beneficial effect in providing a plenty benefit in analyzing the market data and understanding customers’behavior.An important motive of using such techniques is maximizing the business profit as well as matching the exact customer needs as closely as possible.In this survey paper,we discussed several applications and methods of MBA based on ARL.Also,we reviewed some association rule learning measurements including trust,lift,leverage,and others.Furthermore,we discuss some open issues and future topics in the area of market basket analysis and association rule learning.

Classification of Mineral Resources Associated and Accompanied with Coal Measures

The paper discusses the concept of mineral resources associated with coal measures. A rational and scientific classification of such mineral resources becomes more necessary with the development of science and technology. A classification scheme is proposed based on compositions and physical properties and the utilization of these associated minerals.

Approach for Multiword Expression Identification in Natural Language Processing

In this paper, the authors are presenting the approach to extract the multiword expression （MWEs） from monolingual corpora. It both validates and generates multiword candidates. The multiword expression provides a list of candidates which are extracted and filtered according to the number of criteria and a set of standard statistical association measures. The generation of the multiword candidates is based on the surface forms, while the validation consists of series of criteria for removing noise using language independent association measures. For generating corpus count, it provides both a corpus indexation facility. Also, this approach allows easy integration with a machine learning tool for thecreation and application of supervised multiword extraction models if annotated data is available. The authors present the use of multiword in a standard configuration, for extracting MWEs from a corpus of general purpose English.

A New Approach for Dispersion Parameters

This paper presents a new approach to identify and estimate the dispersion parameters for bivariate, trivariate and multivariate correlated binary data, not only with scalar value but also with matrix values. For this direction, we present some recent studies indicating the impact of over-dispersion on the univariate data analysis and comparing a new approach with these studies. Following the property of McCullagh and Nelder [1] for identifying dispersion parameter in univariate case, we extended this property to analyze the correlated binary data in higher cases. Finally, we used these estimates to modify the correlated binary data, to decrease its over-dispersion, using the Hunua Ranges data as an ecology problem.

The association of olanzapine-induced metabolic disturbance related measures with TCF7L2 gene expression

Objective To investigate the relationship between olanzapine induced metabolic disturbance related measures and TCF7L2 gene expression.Methods Thirty adult C57BL/61 mice,in accordance with the random number table,were divided into 3 groups that were

Robust Segmentation,Shape Fitting and Morphology Computation of High-Throughput Cell Nuclei

Accurate nuclear classification (e.g., grading of renal cell carcinoma (RCC) biopsy images) is important to better understand fundamental phenomena such as tumor growth. In this paper, an automated pipeline is proposed to quantitatively analyze RCC data. A novel segmentation methodology is firstly used to delineate cell nuclei based on minimum description length (MDL) constrained B-spline curve fitting. From the obtained segmentations, thirteen features are then extracted based on five types of characteristics. These features are used to classify cell nuclei in biopsy images. Associations among nuclei are computed and represented by graphical networks to enable further analysis. Finally, a support vector machine (SVM) based decision-graph classifier is introduced to classify the biopsy images with the purpose of grading. Experimental results on real RCC data show that our SVM-based decision-graph classifier achieves 95.20% of classification accuracy while the SVM classifiers achieve 93.33% of classification accuracy. © 2017, Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg.