http://www.mdpi.com/journal/algorithms/special_issues/data-mining/ Dear Colleagues, In recent years, multi-core and many-core architectures have become the only means for scaling performance. This development, however, has lead to new challenges for application development. Another independent development has been related to cloud computing, where computing hardware and services can be accessed and paid for like utilities. Again, this is creating new challenges in developing scalable applications. This special issue will focus on data mining in the multi-core, many-core and cloud era. Specific topics of interest include: 1. Data Mining algorithms for multi-core and many-core architectures, including GPUs. 2. Data Mining case studies on new emerging architectures. 3. Data Mining algorithms and implementation development with existing cloud infrastructures (including, but not limited to map-reduce and its variants). 4. Data Mining for optimizing and tuning applications on multi-cores and GPGPUs 5. Data Mining for resource provisioning and other performance optimizations in cloud environments. Prof. Dr. Gagan Agrawal, Guest Editor Submission All papers should be submitted to algorithms@mdpi.com. To be published continuously until the deadline and papers will be listed together at the special issue website. Submitted papers should not have been published nor be under consideration for publication elsewhere. All papers are refereed through a peer-review process. A guide for authors is available on the Instructions for Authors page. Algorithms is an international peer-reviewed quarterly journal published by MDPI. Article Processing Charges (APC) will be waived for well prepared manuscripts of invited papers. For the first three volumes of this new journal the APC are of 300 CHF (or 550 CHF per paper for those papers that require extensive additional formatting and/or English corrections). http://www.mdpi.com/1999-4893/3/1/92/ In this paper, we apply various data mining techniques including continuous numeric and discrete classification prediction models of base oils biodegradability, with emphasis on improving prediction accuracy. The results show that highly biodegradable oils can be better predicted through numeric models. In contrast, classification models did not uncover a similar dichotomy. With the exception of Memory Based Reasoning and Decision Trees, tested classification techniques achieved high classification prediction. However, the technique of Decision Trees helped uncover the most significant predictors. A simple classification rule derived based on this predictor resulted in good classification accuracy. The application of this rule enables efficient classification of base oils into either low or high biodegradability classes with high accuracy. For the latter, a higher precision biodegradability prediction can be obtained using continuous modeling techniques. http://www.mdpi.com/1999-4893/3/1/92/ Tue, 23 Feb 2010 00:00:00 CET Algorithms 2010-02-23 3 1 Article 92 99 1999-4893 Base Oils Biodegradability Prediction with Data Mining Techniques 2010-02-23 doi: 10.3390/algor3010092 Sihem Ben Abdelmelek Saloua Saidane Malika Trabelsi