This document discusses the use of latent semantic analysis (LSA) for document clustering. It describes issues with traditional information retrieval systems, defines key concepts like synonymy and polysemy, and explains how LSA addresses these issues by reducing the semantic space. An experiment is described where documents are clustered with and without LSA preprocessing, showing that LSA leads to improved cluster quality metrics like purity, entropy, and average intra-cluster similarity. The study demonstrates LSA can perform comparably to dedicated clustering tools for organizing documents by topic.
This document discusses using the data mining tool WEKA to perform linear regression and clustering on a dataset. WEKA is an open source software that can be used to load data files, perform predictive modeling and data analysis. The document demonstrates using WEKA to create a linear regression model to predict prices based on attributes like BTU/Hr, weight, EER and region. It also shows how to create an EM clustering model in WEKA that clusters the data into 5 groups based on the attributes.
The document discusses using the data mining tool WEKA to perform linear regression and clustering analysis. It provides steps for loading the housing unit dataset and building linear regression and EM clustering models in WEKA. The linear regression output shows the attributes that predict housing unit price. The clustering analysis identifies 5 clusters in the data and provides details on the attribute means and standard deviations for each cluster.
Presentation by the Chief Product and Marketing Officer of Yodle.com, Louis Gagnon, going over the state of the hyperlocalized marketing industry, how Yodle is playing in the market, and the implications for success in the marketplace.
This deck highlights some of the latest research coming from the Local Search Association and provides a preview of the LSA|15 Conference in LA, April 19-22.
The document discusses the Flint water crisis where the city switched its water source in 2014 from the Detroit water system to the Flint River without implementing corrosion control. This caused lead to leach into the drinking water from aging pipes. Independent studies showed high lead levels in water and children's blood, but officials dismissed residents' concerns. The crisis highlighted issues with aging infrastructure, improper water treatment, and environmental racism. Lead exposure can negatively impact childhood development and public health. Similar problems with lead in drinking water have been found in over half of Massachusetts schools tested.
PSLDoc: Protein subcellular localization prediction based on gapped-dipeptide...JIA-MING CHANG
Prediction of protein subcellular localization (PSL) is important for genome annotation, protein function prediction, and drug discovery. Many computational approaches for PSL prediction based on protein sequences have been proposed in recent years for Gram-negative bacteria. We present PSLDoc, a method based on gapped-dipeptides and probabilistic latent semantic analysis (PLSA) to solve this problem. A protein is considered as a term string composed by gapped-dipeptides, which are defined as any two residues separated by one or more positions. The weighting scheme of gapped-dipeptides is calculated according to a position specific score matrix, which includes sequence evolutionary information. Then, PLSA is applied for feature reduction, and reduced vectors are input to five one-versus-rest support vector machine classifiers. The localization site with the highest probability is assigned as the final prediction. It has been reported that there is a strong correlation between sequence homology and subcellular localization (Nair and Rost, Protein Sci 2002;11:2836–2847; Yu et al., Proteins 2006;64:643–651). To properly evaluate the performance of PSLDoc, a target protein can be classified into low- or high-homology data sets. PSLDoc's overall accuracy of low- and high-homology data sets reaches 86.84% and 98.21%, respectively, and it compares favorably with that of CELLO II (Yu et al., Proteins 2006;64:643–651). In addition, we set a confidence threshold to achieve a high precision at specified levels of recall rates. When the confidence threshold is set at 0.7, PSLDoc achieves 97.89% in precision which is considerably better than that of PSORTb v.2.0 (Gardy et al., Bioinformatics 2005;21:617–623). Our approach demonstrates that the specific feature representation for proteins can be successfully applied to the prediction of protein subcellular localization and improves prediction accuracy. Besides, because of the generality of the representation, our method can be extended to eukaryotic proteomes in the future. The web server of PSLDoc is publicly available at http://bio-cluster.iis.sinica.edu.tw/∼bioapp/PSLDoc/.
Finding new friends: A different kind of recommendation systemEva Ward
This document discusses using topic modeling techniques like Non-negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA) to analyze a corpus of 2,000 documents about Seattle in order to discover common topics that could be used to recommend potential hosts to visitors. The analysis identified 12 topics in the documents, including "artsy", "yoga", "hippies", "outdoorsy", and "young professional". The document compares the topics discovered using NMF versus LDA.
The document discusses model-based clustering of bike sharing station usage data from the Velib' system in Paris. It presents an approach using a naive Poisson mixture model to cluster stations based on their temporal usage profiles, represented as count time series. The model assumes stations belong to clusters that capture their weekly and daily usage patterns. Expectation-maximization is used to estimate the model parameters, assigning stations to clusters and identifying cluster-specific temporal profiles. Analysis of results from applying this approach to Velib' data aims to better understand station usage and grouping stations with similar behaviors.
This document discusses using the data mining tool WEKA to perform linear regression and clustering on a dataset. WEKA is an open source software that can be used to load data files, perform predictive modeling and data analysis. The document demonstrates using WEKA to create a linear regression model to predict prices based on attributes like BTU/Hr, weight, EER and region. It also shows how to create an EM clustering model in WEKA that clusters the data into 5 groups based on the attributes.
The document discusses using the data mining tool WEKA to perform linear regression and clustering analysis. It provides steps for loading the housing unit dataset and building linear regression and EM clustering models in WEKA. The linear regression output shows the attributes that predict housing unit price. The clustering analysis identifies 5 clusters in the data and provides details on the attribute means and standard deviations for each cluster.
Presentation by the Chief Product and Marketing Officer of Yodle.com, Louis Gagnon, going over the state of the hyperlocalized marketing industry, how Yodle is playing in the market, and the implications for success in the marketplace.
This deck highlights some of the latest research coming from the Local Search Association and provides a preview of the LSA|15 Conference in LA, April 19-22.
The document discusses the Flint water crisis where the city switched its water source in 2014 from the Detroit water system to the Flint River without implementing corrosion control. This caused lead to leach into the drinking water from aging pipes. Independent studies showed high lead levels in water and children's blood, but officials dismissed residents' concerns. The crisis highlighted issues with aging infrastructure, improper water treatment, and environmental racism. Lead exposure can negatively impact childhood development and public health. Similar problems with lead in drinking water have been found in over half of Massachusetts schools tested.
PSLDoc: Protein subcellular localization prediction based on gapped-dipeptide...JIA-MING CHANG
Prediction of protein subcellular localization (PSL) is important for genome annotation, protein function prediction, and drug discovery. Many computational approaches for PSL prediction based on protein sequences have been proposed in recent years for Gram-negative bacteria. We present PSLDoc, a method based on gapped-dipeptides and probabilistic latent semantic analysis (PLSA) to solve this problem. A protein is considered as a term string composed by gapped-dipeptides, which are defined as any two residues separated by one or more positions. The weighting scheme of gapped-dipeptides is calculated according to a position specific score matrix, which includes sequence evolutionary information. Then, PLSA is applied for feature reduction, and reduced vectors are input to five one-versus-rest support vector machine classifiers. The localization site with the highest probability is assigned as the final prediction. It has been reported that there is a strong correlation between sequence homology and subcellular localization (Nair and Rost, Protein Sci 2002;11:2836–2847; Yu et al., Proteins 2006;64:643–651). To properly evaluate the performance of PSLDoc, a target protein can be classified into low- or high-homology data sets. PSLDoc's overall accuracy of low- and high-homology data sets reaches 86.84% and 98.21%, respectively, and it compares favorably with that of CELLO II (Yu et al., Proteins 2006;64:643–651). In addition, we set a confidence threshold to achieve a high precision at specified levels of recall rates. When the confidence threshold is set at 0.7, PSLDoc achieves 97.89% in precision which is considerably better than that of PSORTb v.2.0 (Gardy et al., Bioinformatics 2005;21:617–623). Our approach demonstrates that the specific feature representation for proteins can be successfully applied to the prediction of protein subcellular localization and improves prediction accuracy. Besides, because of the generality of the representation, our method can be extended to eukaryotic proteomes in the future. The web server of PSLDoc is publicly available at http://bio-cluster.iis.sinica.edu.tw/∼bioapp/PSLDoc/.
Finding new friends: A different kind of recommendation systemEva Ward
This document discusses using topic modeling techniques like Non-negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA) to analyze a corpus of 2,000 documents about Seattle in order to discover common topics that could be used to recommend potential hosts to visitors. The analysis identified 12 topics in the documents, including "artsy", "yoga", "hippies", "outdoorsy", and "young professional". The document compares the topics discovered using NMF versus LDA.
The document discusses model-based clustering of bike sharing station usage data from the Velib' system in Paris. It presents an approach using a naive Poisson mixture model to cluster stations based on their temporal usage profiles, represented as count time series. The model assumes stations belong to clusters that capture their weekly and daily usage patterns. Expectation-maximization is used to estimate the model parameters, assigning stations to clusters and identifying cluster-specific temporal profiles. Analysis of results from applying this approach to Velib' data aims to better understand station usage and grouping stations with similar behaviors.
This document summarizes a presentation on the OpenNLP toolkit. OpenNLP is an open-source Java toolkit for natural language processing. It provides common NLP features like tokenization, sentence segmentation, part-of-speech tagging, and named entity extraction. The presentation discusses how these features work using pre-trained models for different languages. An example is also given showing how OpenNLP could be used to extract tags from a website and display them in a tag cloud. The presentation concludes by providing contact information for the presenter.
A Simple Stochastic Gradient Variational Bayes for Latent Dirichlet AllocationTomonari Masada
This document proposes applying stochastic gradient variational Bayes (SGVB) to latent Dirichlet allocation (LDA) topic modeling to obtain an efficient posterior estimation. SGVB introduces randomness into variational inference for LDA by estimating expectations with Monte Carlo integration and using reparameterization to sample from approximate posterior distributions. Evaluation on several text corpora shows perplexities comparable to existing LDA inference methods, with the potential for faster parallelization using techniques like GPU processing. Future work will explore applying SGVB to other probabilistic document models like correlated topic models.
EM algorithm and its application in probabilistic latent semantic analysiszukun
The document discusses the EM algorithm and its application in Probabilistic Latent Semantic Analysis (pLSA). It begins by introducing the parameter estimation problem and comparing frequentist and Bayesian approaches. It then describes the EM algorithm, which iteratively computes lower bounds to the log-likelihood function. Finally, it applies the EM algorithm to pLSA by modeling documents and words as arising from a mixture of latent topics.
Latent semantic analysis (LSA) is a technique used in natural language processing to analyze relationships between documents and terms by producing concepts related to them. LSA assumes words with similar meanings will occur in similar texts, and uses a documents-terms matrix and singular value decomposition to discover hidden concepts and represent words and documents as vectors in a semantic vector space. Apache OpenNLP is a machine learning toolkit that can be used for various natural language processing tasks like part-of-speech tagging and parsing, and LSA can be seen as part of natural language processing.
Topic modeling is a technique for discovering hidden semantic patterns in large document collections. It represents documents as probability distributions over latent topics, where each topic is characterized by a distribution over words. Two common probabilistic topic models are latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (pLSA). LDA assumes each document exhibits multiple topics in different proportions, with topics modeled as distributions over words. Topic modeling provides dimensionality reduction and can be applied to problems like text classification, collaborative filtering, and computer vision tasks like image classification.
Latent semantic indexing (LSI) allows search engines to determine the topic of a page outside of directly matching search terms. LSI models the contexts in which words are used to find related pages, similar to how humans understand language through context. LSI was introduced to search engines by Applied Semantics and acquired by Google to power adsense through finding related ads. LSI gives search engines the ability to return more relevant results to users by understanding related terms, synonyms, singular/plural forms, and words with similar meanings or roots. Implementing LSI in a website involves developing thematically focused content using related keywords and synonyms throughout to better match user intent.
The document provides an introduction to Probabilistic Latent Semantic Analysis (PLSA). It discusses how PLSA improves on previous Latent Semantic Analysis methods by incorporating a probabilistic framework. PLSA models documents as mixtures of topics and allows words to have multiple meanings. The parameters of the PLSA model, including the topic distributions and word-topic distributions, are estimated using an expectation-maximization algorithm to find the parameters that best explain the observed word-document co-occurrence data.
Faster, More Effective Flowgraph-based Malware ClassificationSilvio Cesare
Silvio Cesare is a PhD candidate at Deakin University researching malware detection and automated vulnerability discovery. His current work extends his Masters research on fast automated unpacking and classification of malware. He presented this work last year at Ruxcon 2010. His system uses control flow graphs and q-grams of decompiled code as "birthmarks" to detect unknown malware samples that are suspiciously similar to known malware, reducing the need for signatures. He evaluated the system on 10,000 malware samples with only 10 false positives. The system provides improved effectiveness and efficiency over his previous work in 2010.
Slides I used in a tutorial on clustering methods with R at an INDUS research network meeting on the 8th October, 2015 (https://sites.google.com/site/indusnetzwerk/events/tagung-berlin). R codes are available at http://rpubs.com/mrkm_a/ClusteringMethodsWithR
This document discusses adapting situational judgement tests (SJTs) for cross-cultural assessment. SJTs measure flexibility of behaviour by presenting work situations and potential responses. The Leadership Judgement Indicator (LJI) measures leadership style adaptability. Two key issues in adapting the LJI cross-culturally are: 1) whether scenarios transport well across cultures, and 2) if response alternatives represent consistent leadership styles. Preliminary studies show the LJI model fits data well across countries, though some scenarios show differential item functioning. Adapting SJTs across cultures raises important measurement questions.
The document provides guidance on conducting factor analysis and principal components analysis. It discusses sample size requirements, tests to check the suitability of the data for factor analysis, determining the number of factors to retain, interpreting the factor solution, computing factor scores, and assessing the reliability of factors using Cronbach's alpha.
Configuring Mahout Clustering Jobs - Frank Scholtenlucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
For more than a decade internet search engines have helped users find documents they are looking for. However, what if users aren't looking for anything specific but want a summary of a large document collection and want to be surprised? One solution to this problem is document clustering. Clustering algorithms group documents that have similar content. Real-life examples of clustering are clustered search results of Google news, or tag clouds which group documents under a shared label. Apache Mahout is a framework for scalable machine learning on top of Apache Hadoop and can be used for large scale document clustering. This talk introduces clustering in general and shows you step-by-step how to configure Mahout clustering jobs to create a tag cloud from a document collection. This talk is suitable for people who have some experience with Hadoop and perhaps Mahout. Knowledge of clustering is not required.
Word2vec works by using documents to train a neural network model to learn word vectors that encode the words' semantic meanings. It trains the model to predict a word's context by learning vector representations of words. It then represents sentences as the average of the word vectors, and constructs a similarity matrix between sentences to score them using PageRank to identify important summary sentences.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document discusses using k-nearest neighbor (k-NN) algorithm for missing data imputation. It compares the performance of mean, median, and standard deviation imputation techniques when combined with k-NN. The techniques are applied to group data of different sizes, and median and standard deviation show better results than mean substitution. Accuracy improves with larger group sizes and higher percentages of missing data. Median and standard deviation imputation have slightly better performance than mean imputation for missing data imputation when combined with k-NN.
Introduction to Machine Learning @ Mooncascade ML CampIlya Kuzovkin
The document discusses machine learning concepts including representing data in a machine-readable format through feature extraction, choosing an algorithm such as decision trees, evaluating accuracy using a confusion matrix, and ensuring a model generalizes well to new data. Key steps are extracting features from raw data to create an instance, selecting an algorithm like decision trees to learn patterns in the data, and evaluating accuracy on new data while avoiding overfitting or imbalanced training data.
This document summarizes techniques for making deep learning models more efficient, including pruning, weight sharing, quantization, low-rank approximation, and Winograd transformations. It provides examples of applying these techniques to convolutional neural networks to reduce model size by up to 49x while maintaining accuracy. Specific techniques discussed include clustering weights to share values, quantizing weights to fewer bits, pruning low-impact connections, and iteratively retraining pruned models. Energy and computation reductions are achieved through smaller, lower-precision models with fewer operations.
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
This document summarizes a presentation on the OpenNLP toolkit. OpenNLP is an open-source Java toolkit for natural language processing. It provides common NLP features like tokenization, sentence segmentation, part-of-speech tagging, and named entity extraction. The presentation discusses how these features work using pre-trained models for different languages. An example is also given showing how OpenNLP could be used to extract tags from a website and display them in a tag cloud. The presentation concludes by providing contact information for the presenter.
A Simple Stochastic Gradient Variational Bayes for Latent Dirichlet AllocationTomonari Masada
This document proposes applying stochastic gradient variational Bayes (SGVB) to latent Dirichlet allocation (LDA) topic modeling to obtain an efficient posterior estimation. SGVB introduces randomness into variational inference for LDA by estimating expectations with Monte Carlo integration and using reparameterization to sample from approximate posterior distributions. Evaluation on several text corpora shows perplexities comparable to existing LDA inference methods, with the potential for faster parallelization using techniques like GPU processing. Future work will explore applying SGVB to other probabilistic document models like correlated topic models.
EM algorithm and its application in probabilistic latent semantic analysiszukun
The document discusses the EM algorithm and its application in Probabilistic Latent Semantic Analysis (pLSA). It begins by introducing the parameter estimation problem and comparing frequentist and Bayesian approaches. It then describes the EM algorithm, which iteratively computes lower bounds to the log-likelihood function. Finally, it applies the EM algorithm to pLSA by modeling documents and words as arising from a mixture of latent topics.
Latent semantic analysis (LSA) is a technique used in natural language processing to analyze relationships between documents and terms by producing concepts related to them. LSA assumes words with similar meanings will occur in similar texts, and uses a documents-terms matrix and singular value decomposition to discover hidden concepts and represent words and documents as vectors in a semantic vector space. Apache OpenNLP is a machine learning toolkit that can be used for various natural language processing tasks like part-of-speech tagging and parsing, and LSA can be seen as part of natural language processing.
Topic modeling is a technique for discovering hidden semantic patterns in large document collections. It represents documents as probability distributions over latent topics, where each topic is characterized by a distribution over words. Two common probabilistic topic models are latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (pLSA). LDA assumes each document exhibits multiple topics in different proportions, with topics modeled as distributions over words. Topic modeling provides dimensionality reduction and can be applied to problems like text classification, collaborative filtering, and computer vision tasks like image classification.
Latent semantic indexing (LSI) allows search engines to determine the topic of a page outside of directly matching search terms. LSI models the contexts in which words are used to find related pages, similar to how humans understand language through context. LSI was introduced to search engines by Applied Semantics and acquired by Google to power adsense through finding related ads. LSI gives search engines the ability to return more relevant results to users by understanding related terms, synonyms, singular/plural forms, and words with similar meanings or roots. Implementing LSI in a website involves developing thematically focused content using related keywords and synonyms throughout to better match user intent.
The document provides an introduction to Probabilistic Latent Semantic Analysis (PLSA). It discusses how PLSA improves on previous Latent Semantic Analysis methods by incorporating a probabilistic framework. PLSA models documents as mixtures of topics and allows words to have multiple meanings. The parameters of the PLSA model, including the topic distributions and word-topic distributions, are estimated using an expectation-maximization algorithm to find the parameters that best explain the observed word-document co-occurrence data.
Faster, More Effective Flowgraph-based Malware ClassificationSilvio Cesare
Silvio Cesare is a PhD candidate at Deakin University researching malware detection and automated vulnerability discovery. His current work extends his Masters research on fast automated unpacking and classification of malware. He presented this work last year at Ruxcon 2010. His system uses control flow graphs and q-grams of decompiled code as "birthmarks" to detect unknown malware samples that are suspiciously similar to known malware, reducing the need for signatures. He evaluated the system on 10,000 malware samples with only 10 false positives. The system provides improved effectiveness and efficiency over his previous work in 2010.
Slides I used in a tutorial on clustering methods with R at an INDUS research network meeting on the 8th October, 2015 (https://sites.google.com/site/indusnetzwerk/events/tagung-berlin). R codes are available at http://rpubs.com/mrkm_a/ClusteringMethodsWithR
This document discusses adapting situational judgement tests (SJTs) for cross-cultural assessment. SJTs measure flexibility of behaviour by presenting work situations and potential responses. The Leadership Judgement Indicator (LJI) measures leadership style adaptability. Two key issues in adapting the LJI cross-culturally are: 1) whether scenarios transport well across cultures, and 2) if response alternatives represent consistent leadership styles. Preliminary studies show the LJI model fits data well across countries, though some scenarios show differential item functioning. Adapting SJTs across cultures raises important measurement questions.
The document provides guidance on conducting factor analysis and principal components analysis. It discusses sample size requirements, tests to check the suitability of the data for factor analysis, determining the number of factors to retain, interpreting the factor solution, computing factor scores, and assessing the reliability of factors using Cronbach's alpha.
Configuring Mahout Clustering Jobs - Frank Scholtenlucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
For more than a decade internet search engines have helped users find documents they are looking for. However, what if users aren't looking for anything specific but want a summary of a large document collection and want to be surprised? One solution to this problem is document clustering. Clustering algorithms group documents that have similar content. Real-life examples of clustering are clustered search results of Google news, or tag clouds which group documents under a shared label. Apache Mahout is a framework for scalable machine learning on top of Apache Hadoop and can be used for large scale document clustering. This talk introduces clustering in general and shows you step-by-step how to configure Mahout clustering jobs to create a tag cloud from a document collection. This talk is suitable for people who have some experience with Hadoop and perhaps Mahout. Knowledge of clustering is not required.
Word2vec works by using documents to train a neural network model to learn word vectors that encode the words' semantic meanings. It trains the model to predict a word's context by learning vector representations of words. It then represents sentences as the average of the word vectors, and constructs a similarity matrix between sentences to score them using PageRank to identify important summary sentences.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document discusses using k-nearest neighbor (k-NN) algorithm for missing data imputation. It compares the performance of mean, median, and standard deviation imputation techniques when combined with k-NN. The techniques are applied to group data of different sizes, and median and standard deviation show better results than mean substitution. Accuracy improves with larger group sizes and higher percentages of missing data. Median and standard deviation imputation have slightly better performance than mean imputation for missing data imputation when combined with k-NN.
Introduction to Machine Learning @ Mooncascade ML CampIlya Kuzovkin
The document discusses machine learning concepts including representing data in a machine-readable format through feature extraction, choosing an algorithm such as decision trees, evaluating accuracy using a confusion matrix, and ensuring a model generalizes well to new data. Key steps are extracting features from raw data to create an instance, selecting an algorithm like decision trees to learn patterns in the data, and evaluating accuracy on new data while avoiding overfitting or imbalanced training data.
This document summarizes techniques for making deep learning models more efficient, including pruning, weight sharing, quantization, low-rank approximation, and Winograd transformations. It provides examples of applying these techniques to convolutional neural networks to reduce model size by up to 49x while maintaining accuracy. Specific techniques discussed include clustering weights to share values, quantizing weights to fewer bits, pruning low-impact connections, and iteratively retraining pruned models. Energy and computation reductions are achieved through smaller, lower-precision models with fewer operations.
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
Genetic programming is used to evolve data matching configurations that maximize accuracy on test data. The algorithm generates random initial configurations, evaluates them on the test data, and uses genetic operations of selection, crossover and mutation to evolve better configurations over generations. On several datasets, the genetic algorithm is able to find configurations that improve accuracy over manual configurations. However, the evolved configurations are not always intuitive and may represent local optima rather than global optima. More techniques from genetic programming literature could help address these issues.
Terminological cluster trees for Disjointness Axiom DiscoveryGiuseppe Rizzo
The document describes a framework for discovering disjointness axioms from semantic web knowledge bases using terminological cluster trees (TCT). It induces TCTs from knowledge bases to cluster individuals, derives concept descriptions for clusters, and proposes disjointness axioms between non-overlapping concept descriptions. An evaluation on several ontologies shows it can rediscover many existing disjointness axioms and propose new plausible ones, with limited inconsistencies introduced.
Machine Learning in a Flash (Extended Edition): An Introduction to Natural La...Kory Becker
This document provides an overview of machine learning algorithms and techniques. It begins by distinguishing machine learning from other types of artificial intelligence. It then describes popular machine learning algorithms including supervised learning algorithms like linear regression, logistic regression, support vector machines, and neural networks as well as unsupervised learning algorithms like k-means clustering and principal component analysis. The document provides examples of how these algorithms can be applied to tasks like text classification, sentiment analysis, and clustering financial data into categories. It emphasizes that machine learning involves finding patterns in data to make predictions without being explicitly programmed.
Cluster analysis is a technique used to group objects based on characteristics they possess. It involves measuring the distance or similarity between objects and grouping those that are most similar together. There are two main types: hierarchical cluster analysis, which groups objects sequentially into clusters; and nonhierarchical cluster analysis, which directly assigns objects to pre-specified clusters. The choice of method depends on factors like sample size and research objectives.
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
The document reports on experiments conducted with a multiagent system to understand the roles of self and cooperative learning. In the experiments, one agent started with an initial case base of two cases while others started with 16 cases each. The experiments compared learning using only self-learning versus using both self-learning and cooperative learning. The results showed that combining self-learning and cooperative learning generally led to a wider coverage of problem descriptors than only self-learning. It also introduced more diversity into the case bases. Additionally, cooperative learning was found to bring about higher utility and difference gains to the case bases than self-learning alone.
The document discusses the application of artificial intelligence techniques in bioinformatics, providing examples of how symbolic machine learning, nearest neighbor approaches, clustering, and identification trees can be used. It also outlines some of the major challenges in applying AI to bioinformatics, including the need for cross-disciplinary collaboration and developing methods to intelligently analyze and interpret the large amounts of biological data being generated.
Paper and pencil_cosmological_calculatorSérgio Sacani
The document describes a paper-and-pencil cosmological calculator designed for the ΛCDM cosmological model. The calculator contains nomograms (graphs) for quantities like redshift, distance, size, age, and more for different redshift intervals up to z=20. It is based on cosmological parameters from the Planck mission of H0=67.15 km/s/Mpc, ΩΛ=0.683, and Ωm=0.317. To use the calculator, the user finds a known value and reads off other quantities at the same horizontal level.
The document discusses functions in R and their arguments. It explains that functions have formal arguments that may have default values, and arguments can be matched by position or by name. It also demonstrates using the psych package to calculate descriptive statistics and visualize the iris data grouped by species.
1. Roseline Antai
Chris Fox
Udo Kruchswitz
University of Essex, UK.
2. Problems in retrieval systems
Latent Semantic Analysis
Clustering
Experiment
Results
Evaluation
Conclusion
3. Information retrieval systems which use
traditional search approaches are plagued by
issues like:
Noise
Polysemy
Synonymy
Leading to reduced accuracy in retrieved
documents
4. What is Synonymy?
Simply :- the semantic relation that holds
between two words that can (in a given context)
express the same meaning.
Arises from the diversity which exist in the words
people use to define or express the same object
or concept.
Less than 20% of the instances in which two
people use the same major keyword for a certain
object (Deerwester et al ,1990).
Example :- “Automobiles” , “cars”, “vehicles”.
5. What is polysemy?
Simply:- the ambiguity of an individual word or
phrase that can be used (in different contexts) to
express two or more different meanings.
A condition in which words have more than one
unique meaning (Deerwester,1990).
Example:- the word “bank”.
6. A statistical information retrieval
technique, designed to reduce the problems of
polysemy and synonymy in information retrieval
(Hong, 2000).
A technique used for automatic indexing and
retrieval, which takes advantage of the semantic
structure in correlating terms with
documents, to improve the retrieval of
documents of relevance to a certain query .
Designed to solve the problems encountered in
keyword matching systems.
(Deerwester et al, 1990).
7. Defined also as a method used for comparing
texts, through the use of a vector-based
representation, learned from the body of the
documents. Used to create vector-based
representations of texts claimed to capture
the semantic content of such texts (Weimer-
Hastings, 1999).
8. Improves upon the traditional Vector space
model.
Is concerned with dimension reduction
Identified as the major strength of LSA, defined
by Dumais et al (1988) as a technique bearing a
close resemblance to eigenvector decomposition
and factor analysis, which takes a large matrix
say, „X‟, which is the association matrix of terms
to documents, and then decomposes this matrix
into a set of orthogonal factors, usually in the
range of 50-150 factors, which can yield an
approximate of the original matrix if linearly
combined.
9. SVD creates a semantic space from the
original matrix, decomposes it into the left
and right singular vector matrices, and a
diagonal matrix of singlular values.
The semantic space is made up of a term by
concept space, which is the left singular vector
matrix, the concept by document space, which is
the right singular vector matrix, and the third
matrix, the concept by concept space, which is
the diagonal matrix.
Paulsen and Ramampiaro (2009)
10. X ≈X^ =USVT
S – diagonal matrix of Singular values.
U- Term by concept space matrix.
V- Document by concept space matrix.
11. Probabilistic Latent Semantic Analysis (PLSA)
An approach to automated document indexing
based on the statistical latent class model for
factor analysis for count data (Hofman,1999).
More solid statistical foundation
Defines proper generative data model
Latent Dirichlet Allocation (LDA)
Generative probabilistic model
Documents represented as random mixtures over
latent topics.
12. Grouping together objects based their similarity to
each other.
A process of splitting a set of objects into a set of
structured sub-classes , bearing a strong similarity to
each other, such that they can be safely treated as a
group. These sub-classes are referred to as clusters
(Zaiane, 1999).
Document clustering :- A procedure used to
divide documents based on a certain
criterion, like topics, with the expectation that
the clustering process should recognize these
topics, and subsequently place the documents in
the categories to which they belong (Csorba and
Vajk, 2006).
14. Document collection
comprising 118 documents from four topic areas
; deontics, semantics, evolutionary computing
and imperatives.
Converted from all formats to text files
Split into two equal parts; 59 documents
(training set) and 59 documents (test set).
jLSIlibrary used for latent sematic analysis.
Stop words removed.
15. Clustering (Using CLUTO)
CLUTO ; a clustering toolkit consisting of a suite
of clustering algorithms (partitional and
agglomerative, hierarchical and non-
hierarchical).
Used for clustering both high and low
dimensional datasets and analyszing features of
derived clusters(Karypis, 2003).
Uses two standalone programs – vcluster and
scluster.
16. Baseline
Clustering of the test set and training set using
CLUTO only, without carrying out LSA.
Optimal dimensionality
The concept of the existence of an optimal
dimensionality for a document collection of a
certain size investigated.
Dimensions used ranged from 2 to 50 dimensions
21. Purity and Entropy
Entropy is concerned with the distribution of the
different classes of documents within each cluster.
Purity looks at the extent to which a particular
cluster contains documents which are mainly from
one class
(Zhao and Karypis,2003).
Low entropy and high purity values indicate a good
clustering solution.
Precision, Recall and F-measure
Isim
Displays the average similarity between the objects
of each cluster.
22. Baseline clustering results without LSA
Cid Size Isim Sem Imp Deo Evo
0 19 +0.169 1 17 1 0
1 14 +0.159 0 1 1 12
2 12 +0.146 1 1 10 0
3 14 +0.136 9 1 4 0
23. Clustering
results from the test set at 5
dimensions
Cid Size Isim Sem Imp Deo Evo
0 25 0.820 6 10 9 0
1 12 0.627 0 2 1 9
2 16 0.771 3 7 5 1
3 6 0.615 3 1 0 2
24. Cluster 0
Feature Col 1 Col 2 Col 3 Col 4 Col 5
% 20.5 9.8 3.2 2.3 2.0
Cluster 1
Feature Col 1 Col 2 Col 3 Col 4 Col 5
% 9.2 6.4 4.0 3.8 3.3
Cluster 2
Feature Col 1 Col 2 Col 3 Col 4 Col 5
% 8.9 8.1 6.7 6.1 4.5
Cluster 3
Feature Col 1 Col 2 Col 3 Col 4 Col 5
% 9.1 3.4 3.2 2.5 2.4
25. Cluster 0
Feature Col 1 Col 2 Col 3 Col 4 Col 5
% 34.0 32.3 20.7 7.6 5.4
Cluster 1
Feature Col 1 Col 2 Col 3 Col 4 Col 5
% 49.2 26.9 15.1 5.7 3.1
Cluster 2
Feature Col 1 Col 2 Col 3 Col 4 Col 5
% 47.5 27.9 22.7 1.2 0.7
Cluster 3
Feature Col 1 Col 2 Col 3 Col 4 Col 5
% 45.5 41.9 6.4 4.2 2.0
26. Repeat the experiment
On a larger corpus
On a corpus with more similar topics
27. The aim of this work was to cluster a
document set into their respective topic
areas.
To investigate how LSA will perform in
clustering, in comparison to a ready made
clustering software.
LSA gave results which were comparable with
the results from CLUTO.
28. The clusters obtained using LSA had higher
Isim values, in comparison to the baseline,
indicating that the internal cluster similarity
is higher when LSA is used.
The descriptive features produced when LSA
is used give a higher percentage of within
cluster similarity that a feature can explain,
than when LSA is not used
It would be very ambitious to conclude that
LSA does give better results, given the size of
the data set, but LSA did give a
commendable performance.
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