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The problem of multi-instance multi-label learning (MIML) requires a bag of instances to be assigned a set of labels most relevant to the bag as a whole. The problem finds numerous applications in machine learning, computer vision, and natural language processing settings where only partial or distant supervision is available. We present a novel method for optimizing multivariate performance measures in the MIML setting. Our approach MIML-perf uses a novel plug-in technique and offers a seamless way to optimize a vast variety of performance measures such as macro and micro-F measure, average precision, which are performance measures of choice in multi-label learning domains. MIML-perf offers two key benefits over the state of the art. Firstly, across a diverse range of benchmark tasks, ranging from relation extraction to text categorization and scene classification, MIML-perf offers superior performance as compared to state of the art methods designed specifically for these tasks. Secondly, MIML-perf operates with significantly reduced running times as compared to other methods, often by an order of magnitude or more.

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Abstract-Clustering is partitioning of data s et into subsets (clusters), so that the data in each subset share some co mmon t rait. In this paper, an algorith m has been proposed based on Fuzzy C-means clustering technique for prediction of adsorption of cadmiu m by hematite. The original data elements have been used for clustering the random data set. The random data have been generated within the minimu m and maximu m value of test data. The proposed algorithm has been applied on random dataset considering the original data set as initial cluster center. A threshold value has been taken to make the boundary around the clustering center. Finally, after execution of algorith m, mod ified cluster centers have been computed based on each initial cluster center. The modified cluster centers have been treated as predicted data set. The algorith m has been tested in prediction of adsorption of cad miu m by hematite. The error has been calculated between the original data and predicted data. It has been observed that the proposed algorithm has given better result than the previous applied methods.