Different Approaches for Missing Data Handling in Fuzzy Clustering: A Review

2020 ◽  
Vol 13 (6) ◽  
pp. 833-846
Author(s):  
Sonia Goel ◽  
Meena Tushir
Keyword(s):  
Missing Data ◽  
Fuzzy Clustering ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Linear Interpolation ◽  
Performance Criteria ◽  
Data Sets ◽  
Data Set ◽  
Fcm Clustering ◽  
Missing Attributes

Introduction: Incomplete data sets containing some missing attributes is a prevailing problem in many research areas. The reasons for the lack of missing attributes may be several; human error in tabulating/recording the data, machine failure, errors in data acquisition or refusal of a patient/customer to answer few questions in a questionnaire or survey. Further, clustering of such data sets becomes a challenge. Objective: In this paper, we presented a critical review of various methodologies proposed for handling missing data in clustering. The focus of this paper is the comparison of various imputation techniques based FCM clustering and the four clustering strategies proposed by Hathway and Bezdek. Methods: In this paper, we imputed the missing values in incomplete datasets by various imputation/ non-imputation techniques to complete the data set and then conventional fuzzy clustering algorithm is applied to get the clustering results. Results: Experiments on various synthetic data sets and real data sets from UCI repository are carried out. To evaluate the performance of the various imputation/ non-imputation based FCM clustering algorithm, several performance criteria and statistical tests are considered. Experimental results on various data sets show that the linear interpolation based FCM clustering performs significantly better than other imputation as well as non-imputation techniques. Conclusion: It is concluded that the clustering algorithm is data specific, no clustering technique can give good results on all data sets. It depends upon both the data type and the percentage of missing attributes in the dataset. Through this study, we have shown that the linear interpolation based FCM clustering algorithm can be used effectively for clustering of incomplete data set.

scholarly journals Extending the charge-flipping method towards structure solution from incomplete data sets

2007 ◽  
Vol 40 (3) ◽  
pp. 456-462 ◽  
Author(s):  
Lukáš Palatinus ◽  
Walter Steurer ◽  
Gervais Chapuis
Keyword(s):  
Experimental Data ◽  
Missing Data ◽  
Incomplete Data ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Data Sets ◽  
Data Set ◽  
Density Maps ◽  
Structure Solution ◽  
Unknown Structure

The charge-flipping method tends to fail if applied to an incomplete diffraction data set. The reason is artifacts induced in the density maps by Fourier transforming the data. It is shown that the missing data can be sufficiently well approximated on the basis of the Patterson map of the unknown structure optimized by the maximum entropy method (MEM). Structures that could not be solved by the original charge-flipping algorithm can be solved by the proposed method. The method has been tested on experimental data of one inorganic and two organic structures and on several types of missing data. In many cases, up to 50% of missing reflections, or even more, can be tolerated and the structure can still be reconstructed by charge flipping.

The Issue of Missing Values in Data Mining

2011 ◽  
pp. 1102-1109
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Incomplete Data ◽  
Missing Values ◽  
Large Data ◽  
Original Data ◽  
Customer Relationship ◽  
Data Sets ◽  
Data Mining Technique ◽  
Data Set

The essence of data mining is to investigate for pertinent information that may exist in data (often large data sets). The immeasurably large amount of data present in the world, due to the increasing capacity of storage media, manifests the issue of the presence of missing values (Olinsky et al., 2003; Brown and Kros, 2003). The presented encyclopaedia article considers the general issue of the presence of missing values when data mining, and demonstrates the effect of when managing their presence is or is not undertaken, through the utilisation of a data mining technique. The issue of missing values was first exposited over forty years ago in Afifi and Elashoff (1966). Since then it is continually the focus of study and explanation (El-Masri and Fox-Wasylyshyn, 2005), covering issues such as the nature of their presence and management (Allison, 2000). With this in mind, the naïve consistent aspect of the missing value debate is the limited general strategies available for their management, the main two being either the simple deletion of cases with missing data or a form of imputation of the missing values in someway (see Elliott and Hawthorne, 2005). Examples of the specific investigation of missing data (and data quality), include in; data warehousing (Ma et al., 2000), and customer relationship management (Berry and Linoff, 2000). An alternative strategy considered is the retention of the missing values, and their subsequent ‘ignorance’ contribution in any data mining undertaken on the associated original incomplete data set. A consequence of this retention is that full interpretability can be placed on the results found from the original incomplete data set. This strategy can be followed when using the nascent CaRBS technique for object classification (Beynon, 2005a, 2005b). CaRBS analyses are presented here to illustrate that data mining can manage the presence of missing values in a much more effective manner than the more inhibitory traditional strategies. An example data set is considered, with a noticeable level of missing values present in the original data set. A critical increase in the number of missing values present in the data set further illustrates the benefit from ‘intelligent’ data mining (in this case using CaRBS).

A new validity function of FCM clustering algorithm based on intra-class compactness and inter-class separation

2021 ◽  
pp. 1-22
Author(s):  
H.Y. Wang ◽  
J.S. Wang ◽  
L.F. Zhu
Keyword(s):  
Clustering Algorithm ◽  
Optimal Number ◽  
Data Sets ◽  
Data Set ◽  
Class Separation ◽  
Data Similarity ◽  
Fcm Clustering ◽  
Membership Matrix ◽  
Clustering Validity ◽  
Optimal Number Of Clusters

Fuzzy C-means (FCM) clustering algorithm is a widely used method in data mining. However, there is a big limitation that the predefined number of clustering must be given. So it is very important to find an optimal number of clusters. Therefore, a new validity function of FCM clustering algorithm is proposed to verify the validity of the clustering results. This function is defined based on the intra-class compactness and inter-class separation from the fuzzy membership matrix, the data similarity between classes and the geometric structure of the data set, whose minimum value represents the optimal clustering partition result. The proposed clustering validity function and seven traditional clustering validity functions are experimentally verified on four artificial data sets and six UCI data sets. The simulation results show that the proposed validity function can obtain the optimal clustering number of the data set more accurately, and can still find the more accurate clustering number under the condition of changing the fuzzy weighted index, which has strong adaptability and robustness.

scholarly journals K-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cheng Lu ◽  
Shiji Song ◽  
Cheng Wu
Keyword(s):  
Clustering Analysis ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Interval Data ◽  
Similarity Function ◽  
K Nearest Neighbor ◽  
Partial Data ◽  
Missing Attributes ◽  
Ap Clustering

The Affinity Propagation (AP) algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based onK-nearest neighbor intervals (KNNI) for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results.

scholarly journals A SELF-ORGANIZING MAP FOR MIXED CONTINUOUS AND CATEGORICAL DATA

2011 ◽  
pp. 24-32 ◽  
Author(s):  
Nicoleta Rogovschi ◽  
Mustapha Lebbah ◽  
Younès Bennani
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mixed Data ◽  
Categorical Variables ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Public Data ◽  
Self Organizing

Most traditional clustering algorithms are limited to handle data sets that contain either continuous or categorical variables. However data sets with mixed types of variables are commonly used in data mining field. In this paper we introduce a weighted self-organizing map for clustering, analysis and visualization mixed data (continuous/binary). The learning of weights and prototypes is done in a simultaneous manner assuring an optimized data clustering. More variables has a high weight, more the clustering algorithm will take into account the informations transmitted by these variables. The learning of these topological maps is combined with a weighting process of different variables by computing weights which influence the quality of clustering. We illustrate the power of this method with data sets taken from a public data set repository: a handwritten digit data set, Zoo data set and other three mixed data sets. The results show a good quality of the topological ordering and homogenous clustering.

The Application of Fuzzy Clustering Number Algorithm in Network Intrusion Detection

2013 ◽  
Vol 760-762 ◽  
pp. 2220-2223
Author(s):  
Lang Guo
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Local Optimum ◽  
Cluster Number ◽  
Data Set ◽  
Network Intrusion ◽  
Correlation Degree ◽  
Indicator Data ◽  
Detection Effect

In view of the defects of K-means algorithm in intrusion detection: the need of preassign cluster number and sensitive initial center and easy to fall into local optimum, this paper puts forward a fuzzy clustering algorithm. The fuzzy rules are utilized to express the invasion features, and standardized matrix is adopted to further process so as to reflect the approximation degree or correlation degree between the invasion indicator data and establish a similarity matrix. The simulation results of KDD CUP1999 data set show that the algorithm has better intrusion detection effect and can effectively detect the network intrusion data.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

Data Mining with Incomplete Data

2011 ◽  
pp. 293-296 ◽  
Author(s):  
Hai Wang ◽  
Shouhong Wang
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Survey Data ◽  
Incomplete Data ◽  
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).

Incomplete Big Data Distributed Clustering

2014 ◽  
Vol 687-691 ◽  
pp. 1496-1499
Author(s):  
Yong Lin Leng
Keyword(s):  
Big Data ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Design Method ◽  
Complete Data ◽  
Similarity Metrics ◽  
Distributed Clustering ◽  
Computing Technology ◽  
Data Set ◽  
Affinity Propagation Clustering

Partially missing or blurring attribute values make data become incomplete during collecting data. Generally we use inputation or discarding method to deal with incomplete data before clustering. In this paper we proposed an a new similarity metrics algorithm based on incomplete information system. First algorithm divided the data set into a complete data set and non complete data set, and then the complete data set was clustered using the affinity propagation clustering algorithm, incomplete data according to the design method of the similarity metric is divided into the corresponding cluster. In order to improve the efficiency of the algorithm, designing the distributed clustering algorithm based on cloud computing technology. Experiment demonstrates the proposed algorithm can cluster the incomplete big data directly and improve the accuracy and effectively.

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