A Universal Similarity Model for Transactional Data Clustering

Data mining methods are used to extract hidden knowledge from large database. Data partitioning methods are used to group up the relevant data values. Similar data values are grouped under the same cluster. K - means and Partitioning Around Medoids (PAM ) clustering algorithms are used to cluster numerical data. Distance measures are used to estimate the transaction similarity. Data partitioning solutions are identified using the cluster ensembl e models . The ensemble information matrix presents only cluster data point relations. Ensembles based clustering techniques produces final data partition based on incomplete information. Link - based approach improves the conventional matrix by discovering unknown entries through cluster similarity in an ensemble. Link - based algorithm is used for the underlying similarity assessment. Pairwise similarity and binary cluster association matrices summarize the underlying ensemble information. A weighted bipartite graph is formulated from the refined matrix. The graph partitioning technique is applied on the weighted bipartite graph. The Particle Swarm Optimization (PSO) clustering algorithm is a optimization based clustering scheme. It is integrated with the clu ster ensemble model. Binary , categorical and continuous data clustering is supported in the system. The attribute connectivity analysis is optimized for all attributes. Refined cluster - association matrix (RM) is updated with all attribute relationships

Empirical Comparative Analysis of 1-of-K Coding and K-Prototypes in Categorical Clustering

Clustering is a fundamental machine learning application, which partitions data into homogeneous groups. K-means and its variants are the most widely used class of clustering algorithms today. However, the original k-means algorithm can only be applied to numeric data. For categorical data, the data has to be converted into numeric data through 1-of-K coding which itself causes many problems. K-prototypes, another clustering algorithm that originates from the k-means algorithm, can handle categorical data by adopting a different notion of distance. In this paper, we systematically compare these two methods through an experimental analysis. Our analysis shows that K-prototypes is more suited when the dataset is large-scaled, while the performance of k-means with 1-of-K coding is more stable. We believe these are useful heuristics for clustering methods working with highly categorical data

Hybrid Multi Attribute Relation Method for Document Clustering for Information Mining

Text clustering has been widely utilized with the aim of partitioning speci?c documents’ collection into different subsets using homogeneity/heterogeneity criteria. It has also become a very complicated area of research, including pattern recognition, information retrieval, and text mining. In the applications of enterprises, information mining faces challenges due to the complex distribution of data by an enormous number of different sources. Most of these information sources are from different domains which create difficulties in identifying the relationships among the information. In this case, a single method for clustering limits related information, while enhancing computational overheadsand processing times. Hence, identifying suitable clustering models for unsupervised learning is a challenge, specifically in the case of MultipleAttributesin data distributions. In recent works attribute relation based solutions are given significant importance to suggest the document clustering. To enhance further, in this paper, Hybrid Multi Attribute Relation Methods (HMARs) are presented for attribute selections and relation analyses of co-clustering of datasets. The proposed HMARs allowanalysis of distributed attributes in documents in the form of probabilistic attribute relations using modified Bayesian mechanisms. It also provides solutionsfor identifying most related attribute model for the multiple attribute documents clustering accurately. An experimental evaluation is performed to evaluate the clustering purity and normalization of the information utilizing UCI Data repository which shows 25% better when compared with the previous techniques

Clustering ensemble method

A clustering ensemble aims to combine multiple clustering models to produce a better result than that of the individual clustering algorithms in terms of consistency and quality. In this paper, we propose a clustering ensemble algorithm with a novel consensus function named Adaptive Clustering Ensemble. It employs two similarity measures, cluster similarity and a newly defined membership similarity, and works adaptively through three stages. The first stage is to transform the initial clusters into a binary representation, and the second is to aggregate the initial clusters that are most similar based on the cluster similarity measure between clusters. This iterates itself adaptively until the intended candidate clusters are produced. The third stage is to further refine the clusters by dealing with uncertain objects to produce an improved final clustering result with the desired number of clusters. Our proposed method is tested on various real-world benchmark datasets and its performance is compared with other state-of-the-art clustering ensemble methods, including the Co-association method and the Meta-Clustering Algorithm. The experimental results indicate that on average our method is more accurate and more efficient

An Efficient kk-modes Algorithm for Clustering Categorical Datasets

Mining clusters from data is an important endeavor in many applications. The $k$-means method is a popular, efficient, and distribution-free approach for clustering numerical-valued data, but does not apply for categorical-valued observations. The $k$-modes method addresses this lacuna by replacing the Euclidean with the Hamming distance and the means with the modes in the $k$-means objective function. We provide a novel, computationally efficient implementation of $k$-modes, called OTQT. We prove that OTQT finds updates to improve the objective function that are undetectable to existing $k$-modes algorithms. Although slightly slower per iteration due to algorithmic complexity, OTQT is always more accurate per iteration and almost always faster (and only barely slower on some datasets) to the final optimum. Thus, we recommend OTQT as the preferred, default algorithm for $k$-modes optimization.Comment: 16 pages, 10 figures, 5 table

Weighting Policies for Robust Unsupervised Ensemble Learning

The unsupervised ensemble learning, or consensus clustering, consists of finding the optimal com- bination strategy of individual partitions that is robust in comparison to the selection of an algorithmic clustering pool. Despite its strong properties, this approach assigns the same weight to the contribution of each clustering to the final solution. We propose a weighting policy for this problem that is based on internal clustering quality measures and compare against other modern approaches. Results on publicly available datasets show that weights can significantly improve the accuracy performance while retaining the robust properties. Since the issue of determining an appropriate number of clusters, which is a primary input for many clustering methods is one of the significant challenges, we have used the same methodology to predict correct or the most suitable number of clusters as well. Among various methods, using internal validity indexes in conjunction with a suitable algorithm is one of the most popular way to determine the appropriate number of cluster. Thus, we use weighted consensus clustering along with four different indexes which are Silhouette (SH), Calinski-Harabasz (CH), Davies-Bouldin (DB), and Consensus (CI) indexes. Our experiment indicates that weighted consensus clustering together with chosen indexes is a useful method to determine right or the most appropriate number of clusters in comparison to individual clustering methods (e.g., k-means) and consensus clustering. Lastly, to decrease the variance of proposed weighted consensus clustering, we borrow the idea of Markowitz portfolio theory and implement its core idea to clustering domain. We aim to optimize the combination of individual clustering methods to minimize the variance of clustering accuracy. This is a new weighting policy to produce partition with a lower variance which might be crucial for a decision maker. Our study shows that using the idea of Markowitz portfolio theory will create a partition with a less variation in comparison to traditional consensus clustering and proposed weighted consensus clustering