Using Unlabeled Data Set for Mining Knowledge from DDB

In this paper, two algorithms were introduced to describe two algorithms to describe and compare the applying of the proposed technique in the two types of the distributed database system. The First Proposed Algorithm is Homogeneous Distributed Clustering for Classification (HOMDC4C), which aim to learn a classification model from unlabeled datasets distributed homogenously over the network, this is done by building a local clustering model on the datasets distributed over three sites in the network and then build a local classification model based on labeled data that produce from clustering model. In the one computer considered as a control computer, we build a global classification model and then use this model in the future predictive. The Second Proposed Algorithm in Heterogeneous Distributed Clustering for Classification (HETDC4C) aims to build a classification model over unlabeled datasets distributed heterogeneously over sites of the network, the datasets in this algorithm collected in one central computer and then build the clustering model and then classification model. The objective of this work is to use the unlabeled data to introduce a set of labeled data that are useful for build a classification model that can predict any unlabeled instance based on that classification model. This was done by using the Clustering for Classification technique. Then presented this technique in distributed database environment to reduce the execution time and storage space that is required

DENCAST: distributed density-based clustering for multi-target regression

Recent developments in sensor networks and mobile computing led to a huge increase in data generated that need to be processed and analyzed efficiently. In this context, many distributed data mining algorithms have recently been proposed. Following this line of research, we propose the DENCAST system, a novel distributed algorithm implemented in Apache Spark, which performs density-based clustering and exploits the identified clusters to solve both single- and multi-target regression tasks (and thus, solves complex tasks such as time series prediction). Contrary to existing distributed methods, DENCAST does not require a final merging step (usually performed on a single machine) and is able to handle large-scale, high-dimensional data by taking advantage of locality sensitive hashing. Experiments show that DENCAST performs clustering more efficiently than a state-of-the-art distributed clustering algorithm, especially when the number of objects increases significantly. The quality of the extracted clusters is confirmed by the predictive capabilities of DENCAST on several datasets: It is able to significantly outperform (p-value $$<0.05$$ ) state-of-the-art distributed regression methods, in both single and multi-target settings

Heterogeneous Data Clustering Considering Multiple User-provided Constraints

Clustering on heterogeneous networks which consist of multi-typed objects and links has proved to be a useful technique in many scenarios. Although numerous clustering methods have achieved remarkable success, current clustering methods for heterogeneous networks tend to consider only internal information of the dataset. In order to utilize background domain knowledge, we propose a general framework for clustering heterogeneous data considering multiple user-provided constrains. Specifically, we summarize that three types of manual constraints on the object can be used to guide the clustering process. Then we propose the User- HeteClus algorithm to solve the key issues in the case of star-structure heterogeneous data, which incorporating the user constraint into similarity measurement between central objects. Experiments on a real-world dataset show the effectiveness of the proposed algorithm

Multi-View Cluster Analysis With Incomplete Data to Understand Treatment Effects

Multi-view cluster analysis, as a popular granular computing method, aims to partition sample subjects into consistent clusters across different views in which the subjects are characterized. Frequently, data entries can be missing from some of the views. The latest multi-view co-clustering methods cannot effectively deal with incomplete data, especially when there are mixed patterns of missing values. We propose an enhanced formulation for a family of multi-view co-clustering methods to cope with the missing data problem by introducing an indicator matrix whose elements indicate which data entries are observed and assessing cluster validity only on observed entries. In comparison with the simple strategy of removing subjects with missing values, our approach can use all available data in cluster analysis. In comparison with common methods that impute missing data in order to use regular multi-view analytics, our approach is less sensitive to imputation uncertainty. In comparison with other state-of-the-art multi-view incomplete clustering methods, our approach is sensible in the cases of missing any value in a view or missing the entire view, the most common scenario in practice. We first validated the proposed strategy in simulations, and then applied it to a treatment study of heroin dependence which would have been impossible with previous methods due to a number of missing-data patterns. Patients in a treatment study were naturally assessed in different feature spaces such as in the pre-, during-and post-treatment time windows. Our algorithm was able to identify subgroups where patients in each group showed similarities in all of the three time windows, thus leading to the recognition of pre-treatment (baseline) features predictive of post-treatment outcomes

Multi-type clustering and classification from heterogeneous networks

Heterogeneous information networks consist of different types of objects and links. They can be found in several social, economic and scientific fields, ranging from the Internet to social sciences, including biology, epidemiology, geography, finance and many others. In the literature, several clustering and classification algorithms have been proposed which work on network data, but they are usually tailored for homogeneous networks, they make strong assumptions on the network structure (e.g. bi-typed networks or star-structured networks), or they assume that data are independently and identically distributed (i.i.d.). However, in real-world networks, objects can be of multiple types and several kinds of relationship can be identified among them. Moreover, objects and links in the network can be organized in an arbitrary structure where connected objects share some characteristics. This violates the i.i.d. assumption and possibly introduces autocorrelation. To overcome the limitations of existing works, in this paper we propose the algorithm HENPC, which is able to work on heterogeneous networks with an arbitrary structure. In particular, it extracts possibly overlapping and hierarchically-organized heterogeneous clusters and exploits them for predictive purposes. The different levels of the hierarchy which are discovered in the clustering step give us the opportunity to choose either more globally-based or more locally-based predictions, as well as to take into account autocorrelation phenomena at different levels of granularity. Experiments on real data show that HENPC is able to significantly outperform competitor approaches, both in terms of clustering quality and in terms of classification accuracy