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.

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Data Clustering: 50 Years Beyond K-means

Estimation of Correlation and Path Coefficient Studies in Tomato (Lycopersicon esculentum Mill.)

Enhanced optical and electrical properties of zinc: SnO2 nanoparticles for optoelectronic applications

Effect of Tillage, Nutrition Sources and Weed Management on Growth and Productivity of Chickpea (Cicer arietinum L.)

Zinc sulpho selenide has been proposed as a novel buffer layer, alternative to CdS for solar cell applications In this paper Zn-Se-S composite thin layers have been synthesized on ultra clean glass substrate by screen-printing method followed by sintering process Zinc sulphide, zinc selenide and zinc chloride have been used as the basic source material To deposit good quality films, optimum conditions have been determined X-ray diffraction analysis exhibited polycrystalline nature of layers with strong preferential orientation of grains along (200) direction These layers have the wurtzite crystal structure The optical band gap (Eg) of the films has been studied by using reflection spectra in wavelength range 325–600 nm The optical band gap of Zn-Se-S composite thin layer has been found to be in between individual band-gaps of ZnS and ZnSe The DC electrical conductivity of the layers has been measured in vacuum by a two probe technique and was found to be of the order of 10−6 ohm−1cm−1 These properties make these layers suitable for application as a buffer layer in solar cells

Growth and Characterization of Screen-Printed Zinc Sulpho Selenide Composite Thin Layer for Solar Cell Buffer Layer Application

Attitude plays a pivotal role in influencing one’s behavior with respect to a particular psychological object. As corollary of this fact, the farmer’s attitude towards zero tillage technology largely determines the nature and extent of acceptance of scientific farm technology in modernizing agriculture. Thus, attitude of farmers forms essential components for better implementation and success of improved farm technology. It is therefore crucial to identify the attitude of farmers towards zero tillage to take up such step which are required to make ZTT a success. Keeping this in view the present study was designed to construct a standardized scale to measure the attitude of farmers towards zero tillage technology.

Vipin Kumar

Papers

Estimation of Correlation and Path Coefficient Studies in Tomato (Lycopersicon esculentum Mill.)

Enhanced optical and electrical properties of zinc: SnO2 nanoparticles for optoelectronic applications

Effect of Tillage, Nutrition Sources and Weed Management on Growth and Productivity of Chickpea (Cicer arietinum L.)

Growth and Characterization of Screen-Printed Zinc Sulpho Selenide Composite Thin Layer for Solar Cell Buffer Layer Application

A Scale Measure the Attitude of Farmers towards Zero Tillage Technology