Web-based Data Mining Tools Research Articles

Sales Product Clustering Using RFM Calculation Model And K-Means Algorithm on Primskystore

The optimization of data processing procedures can yield high-quality information that is then utilized by business owners in the decision-making process. An effective company plan, particularly in the realm of promotion, holds significant importance for store proprietors in order to foster sustained business expansion. Primskystore is an e-commerce platform operating inside the retail industry, where the efficacy of product sales promotion is perceived to be suboptimal. Sales product advertising continues to revolve around a single sort of product. The objective of this work is to develop a data mining model through the implementation of a web-based application that utilizes the K-means clustering approach and the RFM model. The utilization of the K-Means clustering approach and the RFM model can facilitate the clustering of sales products at retailers. The data mining application employed on this website utilizes the Python programming language, MySQL as the database, and the Unified Modeling Language for system model creation. The findings of this research encompass the development of a web-based data mining tool designed to present the outcomes of product sales clustering within retail establishments. The system has the capability to present the outcomes of the RFM calculation model and KMeans clustering. Specifically, it reveals the presence of three distinct clusters, with cluster 0 accounting for 30% of the data, cluster 1 representing 7.5% of the data, and cluster 2 encompassing 62.5% of the data. This web-based data mining program has the potential to assist retailers in determining an optimal promotional business strategy.

Meta-analysis of gene expression disease signatures in colonic biopsy tissue from patients with ulcerative colitis

Publicly available ulcerative colitis (UC) gene expression datasets from observational studies and clinical trials include inherently heterogeneous disease characteristics and methodology. We used meta-analysis to identify a robust UC gene signature from inflamed biopsies. Eight gene expression datasets derived from biopsy tissue samples from noninflammatory bowel disease (IBD) controls and areas of active inflammation from patients with UC were publicly available. Expression- and meta-data were downloaded with GEOquery. Differentially expressed genes (DEG) in individual datasets were defined as those with fold change > 1.5 and a Benjamini–Hochberg adjusted P value < .05. Meta-analysis of all DEG used a random effects model. Reactome pathway enrichment analysis was conducted. Meta-analysis identified 946 up- and 543 down-regulated genes in patients with UC compared to non-IBD controls (1.2 and 1.7 times fewer up- and down-regulated genes than the median of the individual datasets). Top-ranked up- and down-regulated DEG were LCN2 and AQP8. Multiple immune-related pathways (e.g., ‘Chemokine receptors bind chemokine’ and ‘Interleukin-10 signaling’) were significantly up-regulated in UC, while ‘Biological oxidations’ and ‘Fatty acid metabolism’ were downregulated. A web-based data-mining tool with the meta-analysis results was made available (https://premedibd.com/genes.html). A UC inflamed biopsy disease gene signature was derived. This signature may be an unbiased reference for comparison and improve the efficiency of UC biomarker studies by increasing confidence for identification of disease-related genes and pathways.

GlobalIdentifier: Unexpected Personal Social Content with Data on the Web

The past year has seen a growing public awareness of the privacy risks of social networking through personal information that people voluntarily disclose. A spotlight has accordingly been turned on the disclosure policies of social networking sites and on mechanisms for restricting access to personal information on Facebook and other sites. But this is not sufficient to address privacy concerns in a world where Web-based data mining tools can let anyone infer information about others by combining data from multiple sources. To illustrate this, we are building a demonstration data miner, GlobalInferencer, that makes inferences about an individual?s lifestyle and other behavior. GlobalInferencer uses linked data technology to perform unified searches across Facebook, Flickr, and public data sites. It demonstrates that controlling access to personal information on individual social networking sites is not an adequate framework for protecting privacy, or even for supporting valid inferencing. In addition to access restrictions, there must be mechanisms for maintaining the provenance of information combined from multiple sources, for revealing the context within which information is presented, and for respecting the accountability that determines how information should be used.

Perceived Internet Usage Behaviours as Predictor to Outlier Detection in Students' Communities in Academic Environments

It is important to provide perspectives about the effects of Internet usage on students' personal and social behaviours along with the impacts of these usages on their academic performances. To explore students' Internet usage behaviors and predicting outliers in student's community, we have developed Web based data mining tool named Education Data Miner (EDMiner), which provides user friendly interface for different stockholders of the system including professors and deans. This research study was conducted with a sample of 5210 students from one engineering college in India during 36 months continually. The primary focus of this study is to extract Internet usage pattern of students by exploring proxy server access log files. These patterns were then used for identifying outliers in students' community. We have applied centroid and density based clustering methods to identify outliers. Further, the relationship between Internet usage behaviours and various Academic and Non-academic activities were explored. Based on our results the majority of visited Websites, 35 percent, belongs to Websites under Extra-Curricular category whereas for curricular Websites it is 24 percent. Further, our results also contradict the perception that the Internet usage adversary affects the academic performance. Moreover, our analysis results show higher average time spent on Internet did result into nonparticipation in other activities, which are very essential for the growth of these students. This nonparticipation in other activities may prove to be an indicator for loneliness of these individuals.

Perceived Internet Usage Behaviours as Predictor to Outlier Detection in Students’ Communities in Academic Environments

It is important to provide perspectives about the effects of Internet usage on students' personal and social behaviours along with the impacts of these usages on their academic performances. To explore students' Internet usage behaviors and predicting outliers in student's community, we have developed Web based data mining tool named Education Data Miner (EDMiner), which provides user friendly interface for different stockholders of the system including professors and deans. This research study was conducted with a sample of 5210 students from one engineering college in India during 36 months continually. The primary focus of this study is to extract Internet usage pattern of students by exploring proxy server access log files. These patterns were then used for identifying outliers in students' community. We have applied centroid and density based clustering methods to identify outliers. Further, the relationship between Internet usage behaviours and various Academic and Non-academic activities were explored. Based on our results the majority of visited Websites, 35 percent, belongs to Websites under Extra-Curricular category whereas for curricular Websites it is 24 percent. Further, our results also contradict the perception that the Internet usage adversary affects the academic performance. Moreover, our analysis results show higher average time spent on Internet did result into nonparticipation in other activities, which are very essential for the growth of these students. This nonparticipation in other activities may prove to be an indicator for loneliness of these individuals.

Network analysis for gene discovery in plant‐specialized metabolism

Recent omics technologies provide information on multiple components of biological networks. Web-based data mining tools are continuously being developed. Because genes involved in specialized (secondary) metabolism are often co-ordinately regulated at the transcriptional level, a number of gene discovery studies have been successfully conducted using network analysis, especially by integrating gene co-expression network analysis and metabolomic investigation. In addition, next-generation sequencing technologies are currently utilized in functional genomics investigations of Arabidopsis and non-model plant species including medicinal plants. Systems-based approaches are expected to gain importance in medicinal plant research. This review discussed network analysis in Arabidopsis and gene discovery in plant-specialized metabolism in non-model plants.

Web-based tools for data analysis and quality assurance on a life-history trait database of plants of Northwest Europe

Most data mining techniques have rarely been used in ecology. To address the specific needs of scientists analysing data from a plant trait database developed during the LEDA project, a web-based data mining tool has been developed. This paper presents the DIONE data miner and the project it has been developed in. It addresses the nature of plant trait data from a data mining perspective and points out problems that arise when preparing data for this process. The availability of a large amount of high quality data is an essential prerequisite for successful data mining. To ensure this, a software aided reviewing process has been integrated into the LEDA Traitbase system. The process enables third-party contributors to easily commit their data to LEDA, while assuring adherence of the data to the LEDA standard. The paper concludes with information about data mining results on plant trait data achieved so far and gives an outlook on the applicability of data mining to the fields of ecology.

Web‐Based Tools for Mining the NCI Databases for Anticancer Drug Discovery.

In this paper, we describe the development of a set of integrated Web-based tools for mining the National Cancer Institute's (NCI) anticancer databases for anticancer drug discovery. For data mining, three different correlation algorithms were implemented, which included the commonly used Pearson's correlation algorithm available from the NCI's COMPARE program, the Spearman's and Kendall's correlation algorithms. In addition, we implemented the p-value test to evaluate the significance of the correlation results. These Web-based data mining tools allow robust analysis of the correlation between the in vitro anticancer activity of the drugs in the NCI anticancer database, the protein levels and mRNA levels of molecular targets (genes) in the NCI 60 human cancer cell lines for identification of potential lead compounds for a specific molecular target and for study of the molecular mechanism action of a drug. Examples were provided to identify PKC ligands using a lead compound and to identify potential ErbB-2 inhibitors using the mRNA levels of ErbB-2 in the NCI 60 tumor cell lines.

Web-based tools for mining the NCI databases for anticancer drug discovery.

In this paper, we describe the development of a set of integrated Web-based tools for mining the National Cancer Institute's (NCI) anticancer databases for anticancer drug discovery. For data mining, three different correlation algorithms were implemented, which included the commonly used Pearson's correlation algorithm available from the NCI's COMPARE program, the Spearman's and Kendall's correlation algorithms. In addition, we implemented the p-value test to evaluate the significance of the correlation results. These Web-based data mining tools allow robust analysis of the correlation between the in vitro anticancer activity of the drugs in the NCI anticancer database, the protein levels and mRNA levels of molecular targets (genes) in the NCI 60 human cancer cell lines for identification of potential lead compounds for a specific molecular target and for study of the molecular mechanism action of a drug. Examples were provided to identify PKC ligands using a lead compound and to identify potential ErbB-2 inhibitors using the mRNA levels of ErbB-2 in the NCI 60 tumor cell lines.

A new web-based data mining tool for the identification of candidate genes for human genetic disorders.

To identify the gene underlying a human genetic disorder can be difficult and time-consuming. Typically, positional data delimit a chromosomal region that contains between 20 and 200 genes. The choice then lies between sequencing large numbers of genes, or setting priorities by combining positional data with available expression and phenotype data, contained in different internet databases. This process of examining positional candidates for possible functional clues may be performed in many different ways, depending on the investigator's knowledge and experience. Here, we report on a new tool called the GeneSeeker, which gathers and combines positional data and expression/phenotypic data in an automated way from nine different web-based databases. This results in a quick overview of interesting candidate genes in the region of interest. The GeneSeeker system is built in a modular fashion allowing for easy addition or removal of databases if required. Databases are searched directly through the web, which obviates the need for data warehousing. In order to evaluate the GeneSeeker tool, we analysed syndromes with known genesis. For each of 10 syndromes the GeneSeeker programme generated a shortlist that contained a significantly reduced number of candidate genes from the critical region, yet still contained the causative gene. On average, a list of 163 genes based on position alone was reduced to a more manageable list of 22 genes based on position and expression or phenotype information. We are currently expanding the tool by adding other databases. The GeneSeeker is available via the web-interface (http://www.cmbi.kun.nl/GeneSeeker/).

Integrating web-based data mining tools with business models for knowledge management

As firms begin to implement web-based presentation and data mining tools to enhance decision support capability, the firm's knowledge workers must determine how to most effectively use these new web-based tools to deliver competitive advantage. The focus of this study is on evaluating how knowledge workers integrate these tools into their information and knowledge management requirements. The relationship between the independent variables (web-based data mining software tools and business models) and the dependent variable (strategic performance capabilities) is empirically tested in this study. The results from this study demonstrate the positive interaction effect between the tools and models application on strategic performance capability.