scholarly journals IMPLEMENTASI METODE CENTROID DECOMPOSITION UNTUK PERAMALAN PADA DATA CUACA

2019 ◽  
Vol 1 (1) ◽  
pp. 13-17
Author(s):  
Irfan Pratama
Keyword(s):  
Data Mining ◽  
Missing Values ◽  
Centroid Decomposition

Data mining adalah sebuah fase pencarian pengetahuan pada kumpulan suatu data. Data mining juga adalah sebuah proses ekstraksi dari informasi-informasi dan pengetahuan-pengetahuan yang berguna yang didapat dari kumpulan data yang besar, tidak lengkap, acak, dan ambigu. Berdasarkan pengetahuan tersebut, penelitian ini dilakukan untuk mengetahui apakah metode yang diterapkan oleh peneliti sebelumnya pada penanganan missing values dapat diterapkan pada proses prediksi dengan beberapa penyesuaian. Seiring bertambahnya titik prediksi, hasil dari metode Ekstrapolasi Linear semakin buruk. Dengan kata lain tidak cocok untuk melakukan prediksi jangka menengah hingga panjang, namun dapat dilakukan menggunakan metode Centroid Decomposition.

Comparison of Linguistic Summaries and Fuzzy Functional Dependencies Related to Data Mining

2014 ◽  
pp. 174-203 ◽  
Author(s):  
Miroslav Hudec ◽  
Miljan Vučetić ◽  
Mirko Vujošević
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Relational Databases ◽  
Missing Values ◽  
Expert Knowledge ◽  
Real Data ◽  
Research Area ◽  
Functional Dependencies ◽  
Useful Knowledge ◽  
Important Research Area

Data mining methods based on fuzzy logic have been developed recently and have become an increasingly important research area. In this chapter, the authors examine possibilities for discovering potentially useful knowledge from relational database by integrating fuzzy functional dependencies and linguistic summaries. Both methods use fuzzy logic tools for data analysis, acquiring, and representation of expert knowledge. Fuzzy functional dependencies could detect whether dependency between two examined attributes in the whole database exists. If dependency exists only between parts of examined attributes' domains, fuzzy functional dependencies cannot detect its characters. Linguistic summaries are a convenient method for revealing this kind of dependency. Using fuzzy functional dependencies and linguistic summaries in a complementary way could mine valuable information from relational databases. Mining intensities of dependencies between database attributes could support decision making, reduce the number of attributes in databases, and estimate missing values. The proposed approach is evaluated with case studies using real data from the official statistics. Strengths and weaknesses of the described methods are discussed. At the end of the chapter, topics for further research activities are outlined.

Effective Intelligent Data Mining Using Dempster-Shafer Theory

2008 ◽  
pp. 2943-2963
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Data Mining ◽  
Missing Values ◽  
Uncertain Reasoning ◽  
Worst Case ◽  
Data Mining Techniques ◽  
Imperfect Data ◽  
Dempster Shafer Theory ◽  
External Management ◽  
Shafer Theory

The efficacy of data mining lies in its ability to identify relationships amongst data. This chapter investigates that constraining this efficacy is the quality of the data analysed, including whether the data is imprecise or in the worst case incomplete. Through the description of Dempster-Shafer theory (DST), a general methodology based on uncertain reasoning, it argues that traditional data mining techniques are not structured to handle such imperfect data, instead requiring the external management of missing values, and so forth. One DST based technique is classification and ranking belief simplex (CaRBS), which allows intelligent data mining through the acceptance of missing values in the data analysed, considering them a factor of ignorance, and not requiring their external management. Results presented here, using CaRBS and a number of simplex plots, show the effect of managing and not managing of imperfect data.

scholarly journals Strongly Possible Keys for SQL

2020 ◽  
Vol 9 (2-3) ◽  
pp. 85-99
Author(s):  
Munqath Alattar ◽  
Attila Sali
Keyword(s):  
Data Mining ◽  
Missing Values ◽  
Possible World ◽  
Bipartite Matching ◽  
Matroid Intersection ◽  
Intersection Problem ◽  
Data Mining Approach ◽  
Data Value ◽  
Two Measures ◽  
Matroid Intersection Problem

Abstract Missing data value is an extensive problem in both research and industrial developers. Two general approaches are there to deal with the problem of missing values in databases; they could be either ignored (removed) or imputed (filled in) with new values (Farhangfar et al. in IEEE Trans Syst Man Cybern-Part A: Syst Hum 37(5):692–709, 2007). For some SQL tables, it is possible that some candidate key of the table is not null-free and this needs to be handled. Possible keys and certain keys to deal with this situation were introduced in Köhler et al. (VLDB J 25(4):571–596, 2016). In the present paper, we introduce an intermediate concept called strongly possible keys that is based on a data mining approach using only information already contained in the SQL table. A strongly possible key is a key that holds for some possible world which is obtained by replacing any occurrences of nulls with some values already appearing in the corresponding attributes. Implication among strongly possible keys is characterized, and Armstrong tables are constructed. An algorithm to verify a strongly possible key is given applying bipartite matching. Connection between matroid intersection problem and system of strongly possible keys is established. For the cases when no strongly possible keys hold, an approximation notion is introduced to calculate the closeness of any given set of attributes to be considered as a strongly possible key using the $$g_3$$ g 3 measure, and we derive its component version $$g_4$$ g 4 . Analytical comparisons are given between the two measures.

scholarly journals Methodologies for Imputation of Missing Values in Rice Pest Data

2021 ◽  
pp. 64-73
Author(s):  
V. Jinubala ◽  
P. Jeyakumar
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Missing Values ◽  
Large Data ◽  
Research Field ◽  
Data Set ◽  
Imputation Methods ◽  
Predictive Mean Matching ◽  
Rice Pest ◽  
Better Than

Data Mining is an emerging research field in the analysis of agricultural data. In fact the most important problem in extracting knowledge from the agriculture data is the missing values of the attributes in the selected data set. If such deficiencies are there in the selected data set then it needs to be cleaned during preprocessing of the data in order to obtain a functional data. The main objective of this paper is to analyse the effectiveness of the various imputation methods in producing a complete data set that can be more useful for applying data mining techniques and presented a comparative analysis of the imputation methods for handling missing values. The pest data set of rice crop collected throughout Maharashtra state under Crop Pest Surveillance and Advisory Project (CROPSAP) during 2009-2013 was used for analysis. The different methodologies like Deleting of rows, Mean & Median, Linear regression and Predictive Mean Matching were analysed for Imputation of Missing values. The comparative analysis shows that Predictive Mean Matching Methodology was better than other methods and effective for imputation of missing values in large data set.

scholarly journals An Integrated  Approach for Cancer Survival Prediction Using Data Mining Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ishleen Kaur ◽  
M. N. Doja ◽  
Tanvir Ahmad ◽  
Musheer Ahmad ◽  
Amir Hussain ◽  
...  
Keyword(s):  
Data Mining ◽  
Ovarian Cancer ◽  
Cancer Patients ◽  
Missing Values ◽  
Life Quality ◽  
Advanced Ovarian Cancer ◽  
Machine Learning Algorithms ◽  
Survival Prediction ◽  
Data Mining Techniques ◽  
Using Data

Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients’ survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.

scholarly journals Indepth Analysis of Medical Dataset Mining: A Comparitive Analysis on a Diabetes Dataset Before and After Preprocessing

2019 ◽  
Author(s):  
Latifa Nass ◽  
Stephen Swift ◽  
Ammar Al Dallal
Keyword(s):  
Data Mining ◽  
Error Rate ◽  
Missing Values ◽  
Graphical Representation ◽  
Classification Performance ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Data Set ◽  
Medical Dataset

Most of the healthcare organizations and medical research institutions store their patient’s data digitally for future references and for planning their future treatments. This heterogeneous medical dataset is very difficult to analyze due to its complexity and volume of data, in addition to having missing values and noise which makes this mining a tedious task. Efficient classification of medical dataset is a major data mining problem then and now. Diagnosis, prediction of diseases and the precision of results can be improved if relationships and patterns from these complex medical datasets are extracted efficiently. This paper analyses some of the major classification algorithms such as C4.5 ( J48), SMO, Naïve Bayes, KNN Classification algorithms and Random Forest and the performance of these algorithms are compared using WEKA. Performance evaluation of these algorithms is based on Accuracy, Sensitivity and Specificity and Error rate. The medical data set used in this study are Heart-Statlog Medical Data Set which holds medical data related to heart disease and Pima Diabetes Dataset which holds data related to Diabetics. This study contributes in finding the most suitable algorithm for classifying medical data and also reveals the importance of preprocessing in improving the classification performance. Comparative study of various performances of machine learning algorithms is done through graphical representation of the results. Keywords: Data Mining, Health Care, Classification Algorithms, Accuracy, Sensitivity, Specificity, Error Rate

scholarly journals SVM and Cross-validation using R Studio

2020 ◽  
Vol 10 (1) ◽  
pp. 46-54
Keyword(s):  
Data Mining ◽  
Missing Values ◽  
Cross Validation ◽  
Confusion Matrix ◽  
Tourism Industry ◽  
Support Vector ◽  
Full Dataset ◽  
The Matrix ◽  
Social Media Platforms ◽  
Classification Of Images

Each passing day data is getting multiplied. It is difficult to extract useful information from such big data. Data Mining is used to extract useful information. Data mining is used in majorly all fields like healthcare, marketing, social media platforms and so on. In this paper, data is loaded and preprocessed by dealing with some missing values. The dataset used is of Airbnb, the platform used for lodging and tourism industry. Analyzing the data by plotting correlation using spearman method. Further, applying PCA and Support Vector Machine classification technique on the dataset. There are various applications of SVM, it is used in face-detection, text and hypertext categorization, classification of images, bioinformatics and so on. SVM has high dimensional input space, sparse document vectors and regularization parameters therefore it is appropriate to use SVM. Cross-validation gives more accurate result. The dataset is divided into folds. The end product is the test set which is similar to full dataset. Confusion matrix is evaluated, grid approach is followed for building the matrix at various seeds and kernels (RBF, Polynomial). The aim of this research is to see which is the best kernel for the dataset.

Data Mining with Incomplete Data

2011 ◽  
pp. 526-530 ◽  
Author(s):  
Hai Wang ◽  
Shouhong Wang
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Incomplete Data ◽  
Missing Values ◽  
Knowledge Engineering ◽  
Data Set ◽  
Survey Question ◽  
Data Source ◽  
The Common ◽  
Extent Of Damage

Survey is one of the common data acquisition methods for data mining (Brin, Rastogi & Shim, 2003). In data mining one can rarely find a survey data set that contains complete entries of each observation for all of the variables. Commonly, surveys and questionnaires are often only partially completed by respondents. The possible reasons for incomplete data could be numerous, including negligence, deliberate avoidance for privacy, ambiguity of the survey question, and aversion. The extent of damage of missing data is unknown when it is virtually impossible to return the survey or questionnaires to the data source for completion, but is one of the most important parts of knowledge for data mining to discover. In fact, missing data is an important debatable issue in the knowledge engineering field (Tseng, Wang, & Lee, 2003). In mining a survey database with incomplete data, patterns of the missing data as well as the potential impacts of these missing data on the mining results constitute valuable knowledge. For instance, a data miner often wishes to know how reliable a data mining result is, if only the complete data entries are used; when and why certain types of values are often missing; what variables are correlated in terms of having missing values at the same time; what reason for incomplete data is likely, etc. These valuable pieces of knowledge can be discovered only after the missing part of the data set is fully explored.

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