A Survey on Soft Subspace Clustering

Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces. SC can be classified into hard subspace clustering (HSC) and soft subspace clustering (SSC). While HSC algorithms have been extensively studied and well accepted by the scientific community, SSC algorithms are relatively new but gaining more attention in recent years due to better adaptability. In the paper, a comprehensive survey on existing SSC algorithms and the recent development are presented. The SSC algorithms are classified systematically into three main categories, namely, conventional SSC (CSSC), independent SSC (ISSC) and extended SSC (XSSC). The characteristics of these algorithms are highlighted and the potential future development of SSC is also discussed.Comment: This paper has been published in Information Sciences Journal in 201

A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin

GOGGLES: Automatic Image Labeling with Affinity Coding

Generating large labeled training data is becoming the biggest bottleneck in building and deploying supervised machine learning models. Recently, the data programming paradigm has been proposed to reduce the human cost in labeling training data. However, data programming relies on designing labeling functions which still requires significant domain expertise. Also, it is prohibitively difficult to write labeling functions for image datasets as it is hard to express domain knowledge using raw features for images (pixels). We propose affinity coding, a new domain-agnostic paradigm for automated training data labeling. The core premise of affinity coding is that the affinity scores of instance pairs belonging to the same class on average should be higher than those of pairs belonging to different classes, according to some affinity functions. We build the GOGGLES system that implements affinity coding for labeling image datasets by designing a novel set of reusable affinity functions for images, and propose a novel hierarchical generative model for class inference using a small development set. We compare GOGGLES with existing data programming systems on 5 image labeling tasks from diverse domains. GOGGLES achieves labeling accuracies ranging from a minimum of 71% to a maximum of 98% without requiring any extensive human annotation. In terms of end-to-end performance, GOGGLES outperforms the state-of-the-art data programming system Snuba by 21% and a state-of-the-art few-shot learning technique by 5%, and is only 7% away from the fully supervised upper bound.Comment: Published at 2020 ACM SIGMOD International Conference on Management of Dat

Efficient K-Mean Clustering Algorithm for Large Datasets using Data Mining Standard Score Normalization

In this paper, the clustering and data mining techniques has been introduced. The data mining is useful for extract the useful information from the large database/dataset. For extract the information with efficient factor, the data mining Normalization techniques can be used. These techniques are Min-Max, Z-Scaling and decimal Scaling normalization. Mining of data becomes essential thing for easy searching of data with normalization. This paper has been proposed the efficient K-Mean Clustering algorithm which generates the cluster in less time. Cluster Analysis seeks to identify homogeneous groups of objects based on the values of their attribute. The Z-Score normalization technique has been used with Clustering concept. The number of large records dataset has been generated and has been considered for analyze the results. The existing algorithm has been analyzed by WEKA Tool and proposed algorithm has been implemented in C#.net. The results have been analyzed by generating the timing comparison graphs and proposed works shows the efficiency in terms of time and calculatio

A Probabilistic Exploration of Food Supplementation and Assistance

Food insecurity is a stark threat that grips our country and affects households throughout our country. Dietary insufficiency manifests itself in ways that affect health and public safety. According to researchers, individuals who suffer from food insecurity have a higher risk of aggression, anxiety, suicide ideation and depression. These problems tend to occur unequally distributed among those households with lower income. In this work, an exploratory analysis within these data sets will be performed to examine the socio-economic, biographical, nutritional, and geographical principal components of food insecurity among survey participants and how the US Supplemental Nutrition Assistance Program (SNAP) effects partakers of this study. Relevant statistical and algorithmic tools will be used such as Self organizing maps (SOMs) and hierarchical clustering will be used for cluster analysis in addition to logistic regression and random forests for propensity score matching. Final results show a positive effect on household wellbeing and increased food spending on SNAP participants