Mining from Open Answers in Questionnaire Data
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The document summarizes a system called Survey Analyzer (SA) that analyzes open-ended answers from questionnaire data. SA uses rule analysis through classification and association rules as well as correspondence analysis to automatically summarize open answers and mine useful information. It employs statistical learning methods like stochastic complexity to acquire rules from categorized text and classify new text. SA views each analysis target and its associated open answers to learn rules and analyze relationships between targets and words.
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Selecting a subset of the best solutions among large-scale problems is an important area of research. When the alternative solutions are stochastic in nature, then it puts more burden on the problem. The objective of this paper is to select a set that is likely to contain the actual best solutions with high probability. If the selected set contains all the best solutions, then the selection is denoted as correct selection. We are interested in maximizing the probability of this selection; P(CS). In many cases, the available computation budget for simulating the solution set in order to maximize P(CS) is limited. Therefore, instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure came to put more effort on the solutions that have more impact on the selected set. In this paper, we derive formulas of how to distribute the available budget asymptotically to find the approximation of P(CS). We then present a procedure that uses OCBA with the ordinal optimization (OO) in order to select the set of best solutions. The properties and performance of the proposed procedure are illustrated through a numerical example. Overall results indicate that the procedure is able to select a subset of the best systems with high probability of correct selection using small number of simulation samples under different parameter settings.
Prediction Of Bioactivity From Chemical Structure
The document provides an overview of quantitative structure-activity relationship (QSAR) modeling for predicting bioactivity from chemical structure. It discusses different types of QSAR models for continuous and categorical activity predictions. It also covers topics like molecular descriptors, model validation, and various statistical methods used in QSAR like linear regression, recursive partitioning, naïve Bayesian classifiers, and more. The document aims to give a practical introduction to key concepts in chemoinformatics and QSAR modeling.
Ica 2013021816274759
This document proposes and evaluates a new metaheuristic optimization algorithm called Current Search (CS) and applies it to optimize PID controller parameters for DC motor speed control. The CS is inspired by electric current flow and aims to balance exploration and exploitation. It outperforms genetic algorithm, particle swarm optimization, and adaptive tabu search on benchmark optimization problems, finding better solutions faster. When applied to optimize a PID controller for DC motor speed control, the CS successfully controlled motor speed.
Optimal rule set generation using pso algorithm
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
MINING TRIADIC ASSOCIATION RULES
This document discusses mining triadic association rules from triadic formal contexts. It begins by introducing formal concept analysis and defining dyadic and triadic formal contexts using an example triadic context representing customers, suppliers, and products. It then reviews related work on triadic concept analysis and mining association rules from multi-dimensional datasets. The document proposes an approach to extract triadic association rules by first transforming the triadic context into equivalent dyadic contexts, then using formal concept analysis to produce dyadic concepts, generators, and association rules which are then mapped to triadic association rules.
Sca a sine cosine algorithm for solving optimization problems
The document proposes a new population-based optimization algorithm called the Sine Cosine Algorithm (SCA) for solving optimization problems. SCA creates multiple random initial solutions and uses sine and cosine functions to fluctuate the solutions outward or toward the best solution, emphasizing exploration and exploitation. The performance of SCA is evaluated on test functions, qualitative metrics, and by optimizing the cross-section of an aircraft wing, showing it can effectively explore, avoid local optima, converge to the global optimum, and solve real problems with constraints.
IRJET- Implementation of Automatic Question Paper Generator System
This document describes a proposed system for automatically generating question papers from input documents. The system performs several steps: it converts PDF/document files to text, conducts preprocessing like removing stop words, uses natural language processing and TF-IDF for key phrase extraction, checks phrases against Wikipedia for domain knowledge, generates triplets for questions, and checks the quality of generated questions using linguistic rules. The system aims to make the question paper generation process faster, more randomized and secure compared to traditional manual methods.
MINING TRIADIC ASSOCIATION RULES
The objective of this research is to extract triadic association rules from a triadic formal context
K := (K1, K2, K3, Y) where K1, K2 and K3 respectively represent the sets of objects, properties
(or attributes) and conditions while Y is a ternary relation between these sets. Our approach
consists to define a procedure to map a set of dyadic association rules into a set of triadic ones.
The advantage of the triadic rules compared to the dyadic ones is that they are less numerous
and more compact than the second ones and convey a richer semantics of data. Our approach is
illustrated through an example of ternary relation representing a set of Customers who
purchase theirProducts from Suppliers. The algorithms and approach proposed have been
validated with experimentations on large real datasets.
Don't Treat the Symptom, Find the Cause!.pptx
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.
In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.
slides
This document provides an overview of machine learning algorithms for classification and clustering, addressing privacy issues in applying these algorithms to sensitive data. It discusses techniques for single dataset publishing and distributed analysis across multiple private datasets using cryptographic protocols or data perturbation to preserve privacy. Key algorithms covered include decision trees, naive Bayes, k-nearest neighbors, support vector machines, boosting, and clustering methods like k-means.
One Graduate Paper
Master's paper submitted for degree comparing computational methods for k-means clustering with application to psychology dataset.
Associative Classification: Synopsis
This document discusses a hybrid technique for associative classification. It begins with an introduction to data mining processes like classification and association rule mining. The author then discusses the motivation and objectives of developing a framework to generate classification association rules more efficiently. The proposed methodology involves reviewing existing models, implementing a classification system using association rules in Weka, and comparing the performance to other methods. The facilities required are data mining tools like Weka. Finally, the document provides references that were consulted in the literature survey on associative classification and related techniques.
Kdd by Mr.Sameer Kumar Das
It is the concept of Data mining and knowledge discovery in Databases(KDD)..
BIODATA:
I am sameer kumar das working as an asst.professor in CSE at GATE,Odisha and i contd.PhD in Engineering.Thanks
P1121133727
The document presents a new method called KCGex-SVM for extracting rules from support vector machines (SVMs). It combines weighted kernel k-means clustering, genetic algorithms, and information from SVMs to generate an interpretable rule set from credit screening data. The method was tested on three credit screening datasets and showed improved accuracy over other rule extraction techniques, generating rules with good performance while maintaining comprehensibility.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...
The document presents a mathematical programming approach for selecting important variables in cluster analysis. It formulates a nonlinear binary model to minimize the distance between observations within clusters, using indicator variables to select important variables. The model is applied to a sample dataset of 30 observations across 5 variables, correctly identifying variables 3, 4 and 5 as most important for clustering the observations into two groups. The results are compared to an existing variable selection heuristic, with the mathematical programming approach achieving a 100% correct classification versus 97% for the other method.
A New Extraction Optimization Approach to Frequent 2 Item sets
In this paper, we propose a new optimization approach to the APRIORI reference algorithm (AGR 94) for 2-itemsets (sets of cardinal 2). The approach used is based on two-item sets. We start by calculating the 1- itemets supports (cardinal 1 sets), then we prune the 1-itemsets not frequent and keep only those that are frequent (ie those with the item sets whose values are greater than or equal to a fixed minimum threshold). During the second iteration, we sort the frequent 1-itemsets in descending order of their respective supports and then we form the 2-itemsets. In this way the rules of association are discovered more quickly. Experimentally, the comparison of our algorithm OPTI2I with APRIORI, PASCAL, CLOSE and MAXMINER, shows its efficiency on weakly correlated data. Our work has also led to a classical model of sideby-side classification of items that we have obtained by establishing a relationship between the different sets of 2-itemsets.
A NEW EXTRACTION OPTIMIZATION APPROACH TO FREQUENT 2 ITEMSETS
This document presents a new optimization approach for extracting frequent 2-itemsets from transactional databases. The approach sorts frequent 1-itemsets by support before generating 2-itemsets, aiming to discover association rules more quickly. Experiments show the proposed OPTI2I algorithm performs efficiently on weakly correlated data compared to APRIORI, PASCAL, CLOSE and MAX-MINER. The work also presents a model for side-by-side classification of items based on relationships between 2-itemsets, which could help arrange products in large stores.
A NEW EXTRACTION OPTIMIZATION APPROACH TO FREQUENT 2 ITEMSETS
In this paper, we propose a new optimization approach to the APRIORI reference algorithm (AGR 94) for 2-itemsets (sets of cardinal 2). The approach used is based on two-item sets. We start by calculating the 1-itemets supports (cardinal 1 sets), then we prune the 1-itemsets not frequent and keep only those that are frequent (ie those with the item sets whose values are greater than or equal to a fixed minimum threshold). During the second iteration, we sort the frequent 1-itemsets in descending order of their respective supports and then we form the 2-itemsets. In this way the rules of association are discovered more quickly. Experimentally, the comparison of our algorithm OPTI2I with APRIORI, PASCAL, CLOSE and MAX-MINER, shows its efficiency on weakly correlated data. Our work has also led to a classical model of side-by-side classification of items that we have obtained by establishing a relationship between the different sets of 2-itemsets.
IEEE Datamining 2016 Title and Abstract
For Details, Contact TSYS Academic Projects.
Ph: 9841103123, 044-42607879
Website: http://www.tsys.co.in/
Mail Id: tsysglobalsolutions2014@gmail.com.
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This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
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[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
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These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
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Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
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