For... Fraud detection Purchasing behavior of a credit card owner usually changes when the card is stolen. The data which deviates too much far away from other data is known as an outlier. Calculate the mean of each cluster of the data. DBSCAN An outlier is a data object that deviates significantly from the rest of the objects, as if it were generated by a different mechanism. These Data Mining Multiple Choice Questions (MCQ) should be practiced to improve the skills required for various interviews (campus interview, walk-in interview, company interview), placements, entrance exams and other competitive examinations. This section focuses on "Data Mining" in Data Science. Would you post a list of every one of all your shared pages like your twitter feed, Facebook page or linkedin profile?Take a look at my site - http://web-design.na.by/, My brother suggested I might like this blog. process of making a group of abstract objects into classes of similar objects In my ebook about regression analysis, I detail various methods and tests for identifying outliers in a multivariate context. A great read. Balram Shrivastav. Due to natural deviations in populations. Data Mining Different Types of Clustering - The objects within a group be similar or different from the objects of the other groups. This, however, could result in the loss of important hidden information because one person’s noise could be another person’s signal. By: Prof. Fazal Rehman Shamil Last modified on July 27th, 2020 ... Variance and standard deviation of data in data mining – Click Here Calculator – Click Here. Global outlier — Object significantly deviates from the rest of the data set 2. Cluster analysis is the group's data objects that primarily depend on information found in the data. The 2010 SIAM International Conference on Data Mining Outlier Detection Techniques Hans-Peter Kriegel, Peer Kröger, Arthur Zimek Ludwig-Maximilians-Universität ... of those can be also used for other data types (because they only require a distance measure) Kriegel/Kröger/Zimek: Outlier Detection Techniques (SDM 2010) 11. This is also called as Outlier Mining. Numeric Outlier is the nonparametric outlier detection technique in a one-dimensional feature space. Data Mining - Tasks - Data mining deals with the kind of patterns that can be mined. For example, 28⁰C is an outlier for a Moscow winter, but not an outlier in another context, 28⁰C is not an outlier for a Moscow summer. Also Read: Difference Between Data Warehousing and Data Mining. :-P And, if you are posting on other sites, I would like to keep up with you. The outlier shows variability in an experimental error or in measurement. There are many methods of outlier detection. Data mining is about finding new information from a large group of data. Outliers can be classified into following three categories: Collective Outliers. For ease of presentation within this chapter, we may refer to data objects that are not outliers as “normal” or expected data. In general, outliers can be classified into three categories, namely global outliers, contextual (or conditional) outliers, and collective outliers. Point outliers are the data points that are far from the other distribution of the data. Types of Data Mining. Given the multifaceted nature of multivariate regression, there are numerous types of outliers in that realm. These unexpected data items are considered as outliers or noise. High Dimensional Outlier Detection Methods (high dimensional sparse data), Probabilistic and Statistical Modeling (parametric). Type 1: Global Outliers (also called “Point Anomalies”) A data point is considered a global outlier if its value is far outside the entirety of the data set in which it is found (similar to how “global variables” in a computer program can be accessed by any function in the program). samples that are exceptionally far from the mainstream of data There are various methods of outlier detection. Intuitively, you might think that data “mining” refers to the extraction of new data, but this isn’t the case; instead, data mining is about extrapolating patterns and new knowledge from the data … Outliers can be classified into following three categories: Research is common parlance refers to a research for knowledge. Data Mining Techniques for Outlier Detection: 10.4018/978-1-60960-102-7.ch002: Among the growing number of data mining techniques in various application areas, outlier detection has gained importance in recent times. Outlier detection is quiet familiar area of research in mining of data set. Collective outlier — A subset of data objects collectively deviate signifi… You can not imagine simply how much time I had spent for this info!Thanks!Also visit my blog post; ãã¥ã¦ãã¥ã¦, What's up every one, here every person is sharing such knowledge, thus it's pleasant to read this webpage, and I used to pay a quick visit this website all the time.Also visit my website; ãã¥ã¦ãã¥ã¦, What's Going down i'm new to this, I stumbled upon this I have discovered It absolutely helpful and it has aided me out loads. Such data objects, which are grossly different from or inconsistent with the remaining set of data, are called outliers.

3. The univariate outlier and Multivariate outliers can influence the overall outcome of the data analysis. Outliers in Data mining is a very hot topic in the field of data mining. He was totally right.This post actually made my day. è²¡å¸, I'm really impressed with your writing skills and also with the layout on your weblog. DATABASE This method is used to identify the data items that do not comply with the expected pattern or expected behavior. The data i... Glossary of data mining terms Accuracy Accuracy is an important factor in assessing the success of data mining. Last modified: December 10, 2020 • Reading Time: 6 minutes. Appreciate it!Also visit my homepage; ã¯ãã¨, I constantly spent my half an hour to read this blog's posts all the time along with a cup of coffee.Also visit my website ... ã¯ãªã¹ãã£ã³ã«ãã¿ã³ è²©å£², Good way of describing, and pleasant post to take facts concerning my presentation topic, which i am going to present in institution of higher education.My web-site; ã¯ãã¨, Have you ever considered publishing an e-book or guest authoring on other blogs?I have a blog based upon on the same subjects you discuss and would really like to have you share some stories/information.I know my subscribers would value your work. Thanks!Here is my blog; ã¯ãªã¹ãã£ã³ã«ãã¿ã³, Hurrah! Data mining is the process of looking at large banks of information to generate new information. Data Mining Tools. Outer detection is also called Outlier Analysis or Outlier mining. Is this a paid theme or did you customize it yourself? Once can also define research as a scientific and systematic search for per... An important aspect of an outlier detection technique is the nature of the desired outlier. Below the flowchart represents the flow: In the process discussed above, the… Abnormal buying patterns can character... Unit-I Concept of Research – Importance of Research – Ethics in Research – Selection of Research Topics and Problems – Research in Statist... K-means Is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. Contextual outlier — Object deviates significantly based on a selected context. types of outlier, different approaches to detect outliers, their advantages and disadvantages and applications. What are outliers?

Very often, there exist data objects that do not comply with the general behavior or model of the data. INTRODUCTION Outlier analysis is used in various types of dataset, such as graphical dataset, numerical dataset, Text dataset, and can also be used on the pictures etc. Additionally your site a lot up fast!What host are you the use of? There are two types of Outliers. Following are classes of techniques that were developed to identify outliers by using their unique characteristics (Tan, Steinbach, & Kumar, 2005).Each of these techniques has multiple parameters and, hence, a data point labeled as an outlier in one algorithm may not be an outlier to another. For example, a signal that may indicate the discovery of a new phenomenon for the data set. The tools of data mining act as a bridge between the dataand information from the data. Outliers can be of two kinds: univariate and multivariate. Here we would like to give a brief idea about the data mining implementation process so that the intuition behind the data mining is clear and becomes easy for readers to grasp. Data skewness ... Outliers in Data mining; data skewness; Correlation analysis of numerical data; Does running a blog such as this require a great deal of work?I have very little knowledge of computer programming however I had been hoping to start my own blog in the near future.Anyway, should you have any suggestions or tips for new blog owners please share. In a few blogs, data mining is also termed as Knowledge discovery. Outliers can indicate that the population has a heavy-tailed distribution or when measurement error occurs. Box plots are a hybrid. Abstract: Outlier Detection is one of the major issues in Data Mining; finding outliers from a collection of patterns is a popular problem in the field of data mining. Let’s discuss the outliers. An outlier is a value or point that differs substantially from the rest of the data.. Outliers can look like this: This: Or this: Sometimes outliers might be errors that we want to exclude or an anomaly that we don’t want to include in our analysis. Some of these causes are mentioned below. Either way keep up the excellent quality writing, it's rare to see a great blog like this one these days.Here is my web site :: rodneydurso.com, Its like you read my mind! It defines the objects and their relationships. At last I got a webpage from where I know how to really get useful data regarding my study and knowledge.my web page: ããªã¼ãã¼ã ãã¼ã, This paragraph is actually a fastidious one it assists new internet users, who are wishing in favor of blogging.Feel free to surf to my site :: ããªã¼ãã¼ã, I do not create a leave a response, but I browsed a few of the comments on "Types of Outliers".I do have a few questions for you if it's okay. Some are graphical such as normal probability plots. TheNumeric outliers calculation can be performed by means of the InterQuartile Range (IQR). One example of noise data is when data have a punctuation symbol and suppose we are analyzing the background noise of the voice when doing speech recognition. This type of outlier can be a problem in regression analysis. Index Terms—Data Mining, Outliers, Anomalies, Supervised I. This technique can be used in a variety of domains, such as intrusion, detection, fraud or fault detection, etc. I'm trying to get my blog to rank for some targeted keywords but I'm not seeing very good gains.If you know of any please share. You should look at Yahoo's home page and watch how they create article headlines to get viewers interested.You might try adding a video or a related pic or two to get readers interested about everything've written. ⦁ Contextual outliers: If an individual data instance is anomalous in a specific context or condition (but not otherwise), then it is termed as a contextual outlier. A univariate outlier is a data outlier that differs significantly from one variable. Algorithm to Detect Outlier in data mining. Described in very simple terms, outlier analysis tries to find unusual patterns in any dataset. Data Mining MCQs Questions And Answers. Detecting the I'll certainly be back.Also visit my web blog - ããªã¼ãã¼ã è²¡å¸, I believe what you said made a bunch of sense. A multivariate outlier is an outlier when a combination of values on two or more than two variables have a significant difference. It is a unit less distance measure introduced by P. C. Mah... An important aspect of an outlier detection technique is the nature of the desired outlier. Z-score is a data normalization technique and assumes a Gaussian distribution of the data. So these are the most powerful applications of Data mining. Z-Score In other words, an outlier is a value that escapes normality and can (and probably will) cause anomalies in the results obtained through algorithms and analytical systems. Collective outliers can be subsets of outliers when we introducing the novelties in data. Univariate outliers can be found when looking at a distribution of values in a single feature space. I understand this is off subject but I just needed to ask. Now, if we found that Distance is greater than Threshold, then it is a signal of Outlier. But, think on this, what if you were to write a killer title? What are Outliers? Types of Outliers • Three kinds: global, contextual and collective outliers – A data set may have multiple types of outlier ... Jian Pei: CMPT 741/459 Data Mining -- Outlier Detection (1) 18 . Multivariate outliers can be found in a n-dimensional space (of n-features). As talked about data mining earlier, data mining is a process where we try to bring out the best out of the data. The DBSCAN technique is based on the DBSCAN clustering algorithm. attributes types in data mining. Calculate the distance of the test data from each cluster mean, Find the nearest cluster to the test data. Ther instruments used in the experiments for taking measurements suddenly malfunctioned. Latest posts by Prof. Fazal Rehman Shamil, Comparison of fee structure of Pakistani Universities, TF IDF Cosine similarity Formula Examples in data mining, KNN algorithm in data mining with examples, Analytical Characterization in Data Mining, Data Generalization In Data Mining – Summarization Based Characterization. A multivariate outlier is an outlier when a combination of values on two or more than two variables have a significant difference. I'd really love to be a part of community where I can get feedback from other knowledgeable people that share the same interest. I want my site loaded up as fast as yours lolAlso visit my weblog: ãã¥ã¦ãã¥ã¦, These are actually wonderful ideas in regarding blogging.You have touched some good factors here. If an individual data instance can be considered as anomalous with respect to the rest of … Detection of Univariate Outlier Based On Normal Distribution Data involving only one attribute or variable are called univariate data. I ain't saying your content isn't solid., but suppose you added a title that grabbed folk's attention? They are helpful in many domains like credit card fraud detection, intrusion detection, fault detection etc. Just my opinion, it would bring your posts a little bit more interesting.My web-site: ã¯ãªã¹ãã£ã³ã«ãã¿ã³, Hello, I desire to subscribe for this blog to obtain most up-to-date updates,therefore where can i do it please assist.Also visit my website - ã¯ãã¨, Howdy fantastic website! This type of data mining technique refers to observation of data items in the dataset which do not match an expected pattern or expected behavior. Any way keep up wrinting.Visit my webpage ã¯ãªã¹ãã£ã³ã«ãã¿ã³ è²©å£², Hello! Clustering-based Methods • Normal data belong to large and dense These data objects are outlier and the analysis of outlier data is referred to as outlier mining. Mahalanobis distance is one of the standardized distance measure in statistics. The univariate outlier and Multivariate outliers can influence the overall outcome of the data analysis. There is no rigid mathematical definition of what constitutes an outlier; determining whether or not an observation is an outlier is ultimately a subjective exercise. If you have any suggestions, please let me know. When applied to dat... http://circ.ahajournals.org/cgi/content/full/101/23/e215, http://christianlouboutinoutlet.webeden.info/#christian louboutin outlet, www.christianlouboutinoutletstorex2013.com, Research Methodology - Objectives and Motivation of research, introduction and applications of outliers, Univariate Outlier Detection Based On Normal Distribution, Research Methodology Paper-1 Syllabus for Statistics. I hope to contribute & help other users like its helped me.Good job.My web blog: ã¯ãªã¹ãã£ã³ã«ãã¿ã³, Excellent weblog right here! In other words, an outlier is a data that is far away from an overall pattern of the sample data. Causes of outliers Prediction can also be used for identification of distribution trends based on available data. Some of the outlier detection methods are mentioned below; Numeric Outlier Outliers exhibit a certain set of characteristics that can be exploited to find them. Do you know if they make any plugins to help with Search Engine Optimization? A. Relational Database: If the data is already in the database that can be mined. The outlier is the data that deviate from other data. 3. 1. You seem to know a lot about this, like you wrote the book in it or something.I think that you could do with a few pics to drive the message home a little bit, but other than that, this is excellent blog. The k-means algorithm takes... What are Neural Networks? The problem of outlier detection for data mining is a rich area of research because the sequences are various types and outliers in sequences can be defined in multiple ways and hence there are different problem … Most data mining methods discard outliers noise or exceptions, however, in some applications such as fraud detection, the rare events can be more interesting than the more regularly occurring one and hence, the outlier analysis becomes important in such case. Attributes of data objects should be divided into two groups ⦁ Contextual attributes: defines the context, e.g., time & location ⦁ Behavioral attributes: characteristics of the object, used in outlier evaluation, e.g., temperature Outliers detection can be performed by Z-Score. Outlier Analysis − Outliers may be defined as the data objects that do not comply with the general behavior or model of the data … I mean "Types of Outliers" is kinda plain. Initialize the Threshold value of the data. Others are model-based. Contextual outliers are the outliers just like noisy data. What is an Outlier? An important aspect of an outlier detection technique is the nature of the desired outlier.

3. The univariate outlier and Multivariate outliers can influence the overall outcome of the data analysis. Outliers in Data mining is a very hot topic in the field of data mining. He was totally right.This post actually made my day. è²¡å¸, I'm really impressed with your writing skills and also with the layout on your weblog. DATABASE This method is used to identify the data items that do not comply with the expected pattern or expected behavior. The data i... Glossary of data mining terms Accuracy Accuracy is an important factor in assessing the success of data mining. Last modified: December 10, 2020 • Reading Time: 6 minutes. Appreciate it!Also visit my homepage; ã¯ãã¨, I constantly spent my half an hour to read this blog's posts all the time along with a cup of coffee.Also visit my website ... ã¯ãªã¹ãã£ã³ã«ãã¿ã³ è²©å£², Good way of describing, and pleasant post to take facts concerning my presentation topic, which i am going to present in institution of higher education.My web-site; ã¯ãã¨, Have you ever considered publishing an e-book or guest authoring on other blogs?I have a blog based upon on the same subjects you discuss and would really like to have you share some stories/information.I know my subscribers would value your work. Thanks!Here is my blog; ã¯ãªã¹ãã£ã³ã«ãã¿ã³, Hurrah! Data mining is the process of looking at large banks of information to generate new information. Data Mining Tools. Outer detection is also called Outlier Analysis or Outlier mining. Is this a paid theme or did you customize it yourself? Once can also define research as a scientific and systematic search for per... An important aspect of an outlier detection technique is the nature of the desired outlier. Below the flowchart represents the flow: In the process discussed above, the… Abnormal buying patterns can character... Unit-I Concept of Research – Importance of Research – Ethics in Research – Selection of Research Topics and Problems – Research in Statist... K-means Is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. Contextual outlier — Object deviates significantly based on a selected context. types of outlier, different approaches to detect outliers, their advantages and disadvantages and applications. What are outliers?

Very often, there exist data objects that do not comply with the general behavior or model of the data. INTRODUCTION Outlier analysis is used in various types of dataset, such as graphical dataset, numerical dataset, Text dataset, and can also be used on the pictures etc. Additionally your site a lot up fast!What host are you the use of? There are two types of Outliers. Following are classes of techniques that were developed to identify outliers by using their unique characteristics (Tan, Steinbach, & Kumar, 2005).Each of these techniques has multiple parameters and, hence, a data point labeled as an outlier in one algorithm may not be an outlier to another. For example, a signal that may indicate the discovery of a new phenomenon for the data set. The tools of data mining act as a bridge between the dataand information from the data. Outliers can be of two kinds: univariate and multivariate. Here we would like to give a brief idea about the data mining implementation process so that the intuition behind the data mining is clear and becomes easy for readers to grasp. Data skewness ... Outliers in Data mining; data skewness; Correlation analysis of numerical data; Does running a blog such as this require a great deal of work?I have very little knowledge of computer programming however I had been hoping to start my own blog in the near future.Anyway, should you have any suggestions or tips for new blog owners please share. In a few blogs, data mining is also termed as Knowledge discovery. Outliers can indicate that the population has a heavy-tailed distribution or when measurement error occurs. Box plots are a hybrid. Abstract: Outlier Detection is one of the major issues in Data Mining; finding outliers from a collection of patterns is a popular problem in the field of data mining. Let’s discuss the outliers. An outlier is a value or point that differs substantially from the rest of the data.. Outliers can look like this: This: Or this: Sometimes outliers might be errors that we want to exclude or an anomaly that we don’t want to include in our analysis. Some of these causes are mentioned below. Either way keep up the excellent quality writing, it's rare to see a great blog like this one these days.Here is my web site :: rodneydurso.com, Its like you read my mind! It defines the objects and their relationships. At last I got a webpage from where I know how to really get useful data regarding my study and knowledge.my web page: ããªã¼ãã¼ã ãã¼ã, This paragraph is actually a fastidious one it assists new internet users, who are wishing in favor of blogging.Feel free to surf to my site :: ããªã¼ãã¼ã, I do not create a leave a response, but I browsed a few of the comments on "Types of Outliers".I do have a few questions for you if it's okay. Some are graphical such as normal probability plots. TheNumeric outliers calculation can be performed by means of the InterQuartile Range (IQR). One example of noise data is when data have a punctuation symbol and suppose we are analyzing the background noise of the voice when doing speech recognition. This type of outlier can be a problem in regression analysis. Index Terms—Data Mining, Outliers, Anomalies, Supervised I. This technique can be used in a variety of domains, such as intrusion, detection, fraud or fault detection, etc. I'm trying to get my blog to rank for some targeted keywords but I'm not seeing very good gains.If you know of any please share. You should look at Yahoo's home page and watch how they create article headlines to get viewers interested.You might try adding a video or a related pic or two to get readers interested about everything've written. ⦁ Contextual outliers: If an individual data instance is anomalous in a specific context or condition (but not otherwise), then it is termed as a contextual outlier. A univariate outlier is a data outlier that differs significantly from one variable. Algorithm to Detect Outlier in data mining. Described in very simple terms, outlier analysis tries to find unusual patterns in any dataset. Data Mining MCQs Questions And Answers. Detecting the I'll certainly be back.Also visit my web blog - ããªã¼ãã¼ã è²¡å¸, I believe what you said made a bunch of sense. A multivariate outlier is an outlier when a combination of values on two or more than two variables have a significant difference. It is a unit less distance measure introduced by P. C. Mah... An important aspect of an outlier detection technique is the nature of the desired outlier. Z-score is a data normalization technique and assumes a Gaussian distribution of the data. So these are the most powerful applications of Data mining. Z-Score In other words, an outlier is a value that escapes normality and can (and probably will) cause anomalies in the results obtained through algorithms and analytical systems. Collective outliers can be subsets of outliers when we introducing the novelties in data. Univariate outliers can be found when looking at a distribution of values in a single feature space. I understand this is off subject but I just needed to ask. Now, if we found that Distance is greater than Threshold, then it is a signal of Outlier. But, think on this, what if you were to write a killer title? What are Outliers? Types of Outliers • Three kinds: global, contextual and collective outliers – A data set may have multiple types of outlier ... Jian Pei: CMPT 741/459 Data Mining -- Outlier Detection (1) 18 . Multivariate outliers can be found in a n-dimensional space (of n-features). As talked about data mining earlier, data mining is a process where we try to bring out the best out of the data. The DBSCAN technique is based on the DBSCAN clustering algorithm. attributes types in data mining. Calculate the distance of the test data from each cluster mean, Find the nearest cluster to the test data. Ther instruments used in the experiments for taking measurements suddenly malfunctioned. Latest posts by Prof. Fazal Rehman Shamil, Comparison of fee structure of Pakistani Universities, TF IDF Cosine similarity Formula Examples in data mining, KNN algorithm in data mining with examples, Analytical Characterization in Data Mining, Data Generalization In Data Mining – Summarization Based Characterization. A multivariate outlier is an outlier when a combination of values on two or more than two variables have a significant difference. I'd really love to be a part of community where I can get feedback from other knowledgeable people that share the same interest. I want my site loaded up as fast as yours lolAlso visit my weblog: ãã¥ã¦ãã¥ã¦, These are actually wonderful ideas in regarding blogging.You have touched some good factors here. If an individual data instance can be considered as anomalous with respect to the rest of … Detection of Univariate Outlier Based On Normal Distribution Data involving only one attribute or variable are called univariate data. I ain't saying your content isn't solid., but suppose you added a title that grabbed folk's attention? They are helpful in many domains like credit card fraud detection, intrusion detection, fault detection etc. Just my opinion, it would bring your posts a little bit more interesting.My web-site: ã¯ãªã¹ãã£ã³ã«ãã¿ã³, Hello, I desire to subscribe for this blog to obtain most up-to-date updates,therefore where can i do it please assist.Also visit my website - ã¯ãã¨, Howdy fantastic website! This type of data mining technique refers to observation of data items in the dataset which do not match an expected pattern or expected behavior. Any way keep up wrinting.Visit my webpage ã¯ãªã¹ãã£ã³ã«ãã¿ã³ è²©å£², Hello! Clustering-based Methods • Normal data belong to large and dense These data objects are outlier and the analysis of outlier data is referred to as outlier mining. Mahalanobis distance is one of the standardized distance measure in statistics. The univariate outlier and Multivariate outliers can influence the overall outcome of the data analysis. There is no rigid mathematical definition of what constitutes an outlier; determining whether or not an observation is an outlier is ultimately a subjective exercise. If you have any suggestions, please let me know. When applied to dat... http://circ.ahajournals.org/cgi/content/full/101/23/e215, http://christianlouboutinoutlet.webeden.info/#christian louboutin outlet, www.christianlouboutinoutletstorex2013.com, Research Methodology - Objectives and Motivation of research, introduction and applications of outliers, Univariate Outlier Detection Based On Normal Distribution, Research Methodology Paper-1 Syllabus for Statistics. I hope to contribute & help other users like its helped me.Good job.My web blog: ã¯ãªã¹ãã£ã³ã«ãã¿ã³, Excellent weblog right here! In other words, an outlier is a data that is far away from an overall pattern of the sample data. Causes of outliers Prediction can also be used for identification of distribution trends based on available data. Some of the outlier detection methods are mentioned below; Numeric Outlier Outliers exhibit a certain set of characteristics that can be exploited to find them. Do you know if they make any plugins to help with Search Engine Optimization? A. Relational Database: If the data is already in the database that can be mined. The outlier is the data that deviate from other data. 3. 1. You seem to know a lot about this, like you wrote the book in it or something.I think that you could do with a few pics to drive the message home a little bit, but other than that, this is excellent blog. The k-means algorithm takes... What are Neural Networks? The problem of outlier detection for data mining is a rich area of research because the sequences are various types and outliers in sequences can be defined in multiple ways and hence there are different problem … Most data mining methods discard outliers noise or exceptions, however, in some applications such as fraud detection, the rare events can be more interesting than the more regularly occurring one and hence, the outlier analysis becomes important in such case. Attributes of data objects should be divided into two groups ⦁ Contextual attributes: defines the context, e.g., time & location ⦁ Behavioral attributes: characteristics of the object, used in outlier evaluation, e.g., temperature Outliers detection can be performed by Z-Score. Outlier Analysis − Outliers may be defined as the data objects that do not comply with the general behavior or model of the data … I mean "Types of Outliers" is kinda plain. Initialize the Threshold value of the data. Others are model-based. Contextual outliers are the outliers just like noisy data. What is an Outlier? An important aspect of an outlier detection technique is the nature of the desired outlier.