Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

Soil Chemical Analysis

Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering.

Data clustering: 50 years beyond K-means

This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

/pdf/top-10-algorithms-in-data-mining-70qtv03lc6.pdf

Top 10 algorithms in data mining

The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms in to taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes cluster analysis (unsupervised learning) from discriminant analysis (supervised learning). The objective of cluster analysis is to simply find a convenient and valid organization of the data, not to establish rules for separating future data into categories.

/pdf/data-clustering-50-years-beyond-k-means-2sd3ukqpur.pdf

Data Clustering: 50 Years Beyond K-means

Transmission of electromagnetic waves through a Si-based one dimensional photonic crystal has been investigated. The proposed structure works as an omni-directional reflector for a certain range of wavelength for an angle of incidence up to 55?. The structure works as a narrow band TM-polarization filter for an angle of incidence more than 55?, i.e. a filter which completely blocks TE-polarized waves but allows certain wavelengths of TM-polarized waves. But at an angle of incidence of 89?, the structure works as a multiple narrow band TM-polarization filter even though no defect layer is introduced inside the structure. It is also found that this multiple narrow pass-bands of TM-polarized waves can be tuned to a desired range of wavelength by changing the temperature of the structure.

https://file.scirp.org/pdf/OPJ20120300021_48905708.pdf

Wave Transmission in Dispersive Si-Based One Dimensional Photonic Crystal

Method: This study was done between January 2018 and June 2019, patients with age >18 years with thoraco-lumbar spinal tuberculosis were studied. Relevant X-rays, Mri, Esr, Crp, Vas (Visual Analog Score) and ODI (Owestry Disability Index) was done. Changes in kyphotic angles pre and post op were calculated in lateral views of pain radiographs of dorsolumbar spine via Cobb’s method.

http://www.orthopaper.com/archives/2020/vol6issue1/PartF/5-4-80-361.pdf

Radiological and clinical outcome of conservative management of thoracolumbar spinal tuberculosis in adults

Mahalanobis D2 statistics revealed considerable genetic diversity among the 32 genotype of sorghum. The genotypes grouped into 7 clusters (cluster 1 had 14 genotypes, cluster II had 11 genotypes, cluster VI had 3 genotypes and cluster III, IV, V and cluster VII had 1 genotype each). This envisaged that the genotypes grouped within a particular cluster are more or less genetically similar to each other and apparent. Wide diversity is mainly due to the remaining genotype distributed over rest of the other clusters. The maximum intra cluster distance exhibited for cluster VI (53.36) and lowest intra cluster distance was recorded for cluster I (45.60). The maximum intra cluster distance was revealed between cluster IV and VI (125.10); whereas, minimum inter cluster distance was between clusters I and III (56.97). The hybridization between the genotypes like CPVI-1724, Varsha and SPV-1725 with SSG-59-3; and 2 genotypes viz. Pant Chari-3 and SSG 59-3 with JHV-14 and HC-260 with SSG-59-3, would produce heterotic hybrids and wide spectrum of variability in subsequent generations. The percent contribution leaves per plant (45.16) followed by green fodder yield per day (19.76), internode length (14.31) and leaf breadth (13.31) and most contributed towards genetic divergence.

Genetic Divergence in Forage Sorghum (Sorghum bicolor (L.) Moench).

Similarity Measures for Categorical Data--A Comparative Study

Here we demonstrate the effect of the concentration of hydrogen peroxide solution on the generation of various kinds of oxygen ions, mainly peroxide (O22−) and superoxide (O2−) ions, on the surface of molybdenum trioxide (MoO3) nanobelts. Due to O22− and O2− ions, the mechanism of charge storage shows an interplay between surface-controlled and diffusion-controlled redox reactions. The concentration of H2O2 is indicative of the strength of hydrogen bonding, which significantly affects the concentration or coordination of the surface species (O2−, O22−, Mo5+ and Mo6+) that are available to interact with the electrolyte ions. By altering the concentration of the surface species (IO2−/IO22−, IMo5+/IMo6+), a specific capacitance as high as 210 F g−1 is achieved for MoO3 nanobelts at an applied current density of 0.5 A g−1, with relatively good cycling stability (≈85 %) after 2000 cycles.

Vipin Kumar

Papers

Wave Transmission in Dispersive Si-Based One Dimensional Photonic Crystal

Radiological and clinical outcome of conservative management of thoracolumbar spinal tuberculosis in adults

Genetic Divergence in Forage Sorghum (Sorghum bicolor (L.) Moench).

Similarity Measures for Categorical Data--A Comparative Study

Oxygen‐Ions‐Mediated Pseudocapacitive Charge Storage in Molybdenum Trioxide Nanobelts