PPT-Low-Energy Adaptive Clustering

Hierarchy An EnergyEfficient Communication Protocol for Wireless Microsensor Networks M Aslam hayat Overview Introduction Radio Model Existing Protocols Direct Transmission Minimum Transmission Energy. Shi . Bai. , . Weiyi. Zhang, . Guoliang. . Xue. , . Jian. Tang, and . Chonggang. Wang. University of Minnesota, AT&T Lab, Arizona State University, Syracuse University, NEC Lab. 2012 IEEE INFOCOM. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . University of Cambridge. LCD-WG2, September 1 2010. Pandora PFA . Recent Changes. Recent Changes. This talk will discuss two areas of recent . Pandora . PFA development. :. 1. Forced . clustering. This concerns the behaviour of the reconstruction when the reclustering identifies a poor track-cluster match, which . for . Adaptive . C. ircuit . D. esign. Ang. Lu, . Hao. He, and Jiang Hu. Introduction. Proposed Techniques. Experiment Result. Conclusion. Overview. 2. Design Challenges. Process Variations. Device Aging. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values.. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because . Eva García-. Martín. . eva.garcia.martin@bth.se. Thesis. Make Machine Learning algorithms more energy efficient. Machine. . learning. Algorithms that automatically learn with experience. How can one learn from data and process evolving data in only one pass?.