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.

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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

Achieving sustainable Development Goals (SDGs) is a challenge for both developed and developing countries. Different countries have adopted multifarious policy measures to instate these targets. However, the performance of these countries shows a pattern of development according to their system of governance. The paper magnetizes a cross-country analysis of different nations, segmented on the basis of their system of governance, towards achieving the SDGs. It shows a trend analysis of different SDGs and the extent of challenges in these countries, in the past twenty years (2000-2019). It has been observed that the performance of federal countries is relatively better than the performance of countries with a one-party system and the countries with absolute monarchies. The paper advocates the idea of cooperative federalism and shows how it can play a definitive role in achieving SDGs.

https://www.jetir.org/papers/JETIR2004467.pdf

Sustainable Development Goals: A Cross-Country Analysis

[SNsipmim]Cl a new and highly effective, recyclable nano catalyst for single-pot three-component Mannich type reaction

Genetics of resistance to mustard aphid, Lipaphis erysimi (Kalt), in Indian mustard (Brassica juncea L. Czern & Coss) at different stages of crop.

Pomegranate belongs to the family Punicaceae. The genus „Punica‟ has two species viz., Punica granatum (cultivated pomegranate) and Punica protopunica (wild pomegranate). Pomegranate is basically diploid in nature with 2n=2x=16, 18 chromosomes (Smith, 1979, Darlington and Janakiammal, 1945). The number of chromosome in the somatic complements of cvs.,Dholka, Ganesh, Kandhari, Muscat White and Patiala were found to be 2n=16, while the cv. Double Flower (ornamental type) had 2n=18 (Nath and Randhawa, 1959a). The chromosome number in cvs. Vellodu and Kashmiri were found to be 2n=2x=18 (Raman, et al., 1963). Botanically the type of pomegranate fruit is berry balausta. The pomegranate (Punica granatum L.) is believed to be originated from Iran (Primary centre of Origin). Besides, it is widely prevalent in Afghanistan, Pakistan and India, the Secondary Centers of Origin (DeCandolle, 1967). The usage of pomegranate is deeply embedded in human history with references in many ancient cultures about its use in food and medicine (Holland et al., 2009). It is one of the oldest known edible fruits and is associated with ancient civilizations of the Middle East. India ranks first in the world with respect to pomegranate International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 10 (2020) Journal homepage: http://www.ijcmas.com

Vipin Kumar

Papers

Sustainable Development Goals: A Cross-Country Analysis

[SNsipmim]Cl a new and highly effective, recyclable nano catalyst for single-pot three-component Mannich type reaction

Genetics of resistance to mustard aphid, Lipaphis erysimi (Kalt), in Indian mustard (Brassica juncea L. Czern & Coss) at different stages of crop.

Effect of Different Growing Conditions on Rooting and Growth of Stem Cuttings in Pomegranate (Punica granatum L.)

Effect of Thermal Annealing on Structure and Optical Band Gap of Amorphous Se66Te25Sb9 Thin Film