Mutual Clustering Research Articles

A Smart-Mutual Decentralized System for Long-Term Care

Existing caretakers of long-term care are assigned constrainedly and randomly to taking care of older people, which could lead to issues of shortage of manpower and poor human quality, especially the proportion of older people increases year after year to let long-term care become more and more important. In addition, due to different backgrounds, inadequate caregivers may cause older people to suffer from spiritual alienation under the current system. Most of the existing studies present a centralized architecture, but even if technology elements are incorporated, such as cloud center services or expert systems, it is still impossible to solve the above-mentioned challenges. This study moves past the centralized architecture and attempts to use the decentralized architecture with Artificial Intelligence and Blockchain technology to refine the model of providing comprehensive care for older people. Using the proposed mapping mutual clustering algorithm in this study, the positions of caregivers and older people can be changed at any time based on the four main background elements: risk level, physiology, medical record, and demography. In addition, this study uses the proposed long-term care decentralized architecture algorithm to solve the stability of care records with transparency to achieve the effect of continuous tracking. Based on previous records, it can also dynamically change the new matching mode. The main contribution of this research is the proposal of an innovative solution to the problem of mental alienation, insufficient manpower, and the privacy issue. In addition, this study evaluates the proposed method through practical experiments. The corporation features have been offered and evaluated with user perceptions by a one-sample t-test; the proposed algorithm to the research model also has been compared with not putting it into the model through ANOVA analysis to get that all hypotheses are supported. The results reveal a high level of accuracy of the proposed mutual algorithm forecasting and positive user perceptions from the post-study questionnaire. As an emerging research topic, this study undoubtedly provides an important research basis for scholars and experts who are interested in continued related research in the future.

Visual analytics and information extraction of geological content for text-based mineral exploration reports

A large amount of continuously increasing textual geoscience data is stored and not fully utilized. Text mining enables the discovery and analysis of valuable information,and presents valuable insights hidden in geological texts. This research aims to use text mining and visualization techniques to obtain content words -for the purpose of visually analyzing geological reports. The framework proposed in this study can enable researchers to quickly understand key information and improve the transmission efficiency of geological reports. First, we implemented an improved keyword extraction algorithm comprising the term frequency-inverse document frequency and word length to improve the accuracy of geological keyword extraction. Second, we extracted and visualized the relative importance as well as the links between content words that can represent the key information of geoscience reports using word-level information analysis and multidimensional scaling analysis. Finally, the keyword relevance and mutual clustering relations were visualized through graphs to provide an intuitive representation of the current state of the reports.

Multiple kernel clustering with late fusion consensus local graph preserving

Multiple kernel clustering (MKC) methods aim at integrating an optimal partition from a set of precalculated kernel matrices. Though achieving success in various applications, we observe that existing MKC methods: (i) lack of representation flexibility; and (ii) do not considerably preserve the locality structure in partition space. These issues may adversely affect the learning procedure of MKC, leading to unsatisfying clustering performance. In this paper, we propose a late fusion MKC method with local graph refinement to address the aforementioned issues. Different from existing MKC mechanisms, our method unifies the traditional weighted multiple kernel k-means, kernel partition, and graph construction into a single optimization procedure. The local graph is utilized to preserve the locality information in partition space and therefore all of the counterparts can be boosted for mutual clustering improvements. By this way, our approach enhances the local graph structure in partition space and enjoys more flexible kernel representations, leading to significant clustering improvements. Moreover, a three-step alternate algorithm is developed to solve the resultant optimization problem with proved convergence. Extensive experiments are conducted on several multiple kernel benchmark datasets to compare the proposed algorithm with the state-of-the-art ones, and the results well demonstrate its effectiveness and superiority.

Information theoretic generalized Robinson-Foulds metrics for comparing phylogenetic trees.

The Robinson-Foulds (RF) metric is widely used by biologists, linguists and chemists to quantify similarity between pairs of phylogenetic trees. The measure tallies the number of bipartition splits that occur in both trees-but this conservative approach ignores potential similarities between almost-identical splits, with undesirable consequences. 'Generalized' RF metrics address this shortcoming by pairing splits in one tree with similar splits in the other. Each pair is assigned a similarity score, the sum of which enumerates the similarity between two trees. The challenge lies in quantifying split similarity: existing definitions lack a principled statistical underpinning, resulting in misleading tree distances that are difficult to interpret. Here, I propose probabilistic measures of split similarity, which allow tree similarity to be measured in natural units (bits). My new information-theoretic metrics outperform alternative measures of tree similarity when evaluated against a broad suite of criteria, even though they do not account for the non-independence of splits within a single tree. Mutual clustering information exhibits none of the undesirable properties that characterize other tree comparison metrics, and should be preferred to the RF metric. The methods discussed in this article are implemented in the R package 'TreeDist', archived at https://dx.doi.org/10.5281/zenodo.3528123. Supplementary data are available at Bioinformatics online.

Mutual clustering on comparative texts via heterogeneous information networks

Currently, many intelligence systems contain the texts from multi-sources, e.g., bulletin board system posts, tweets and news. These texts can be “comparative” since they may be semantically correlated and thus provide us with different perspectives toward the same topics or events. To better organize the multi-sourced texts and obtain more comprehensive knowledge, we propose to study the novel problem of Mutual Clustering on Comparative Texts (MCCT), which aims to cluster the comparative texts simultaneously and collaboratively. The MCCT problem is difficult to address because 1) comparative texts usually present different data formats and structures and thus, they are hard to organize and 2) there lacks an effective method to connect the semantically correlated comparative texts to facilitate clustering them in an unified way. To this aim, in this paper, we propose a Heterogeneous Information Network-based Text clustering framework HINT. HINT first models multi-sourced texts (e.g. news and tweets) as heterogeneous information networks by introducing the shared “anchor texts” to connect the comparative texts. Next, two similarity matrices based on HINT as well as a transition matrix for cross-text-source knowledge transfer are constructed. Comparative texts clustering are then conducted by utilizing the constructed matrices. Finally, a mutual clustering algorithm is also proposed to further unify the separate clustering results of the comparative texts by introducing a clustering consistency constraint. We conduct extensive experimental on three tweets-news datasets, and the results demonstrate the effectiveness and robustness of the proposed method in addressing the MCCT problem.

Mutual clustering coefficient-based suspicious-link detection approach for online social networks

Online social networks (OSNs) are trendy and rapid information propagation medium on the web where millions of new connections either positive such as acquaintance, or negative such as animosity, are being established every day around the world. The negative links (or sometimes referred to as harmful connections) are mostly established by fake profiles as they are being created by minds with ill aims. Detecting negative (or suspicious) links within online users can better aid in the mitigation of fake profiles from OSNs.A modified clustering coefficient formula, named as MutualClusteringCoefficient represented byMcc, is introduced to quantitatively measure the connectivity between the mutual friends of two connected users in a group. In this paper, a classification system based on mutual clustering coefficient and profile information of users has been presented to detect the suspicious links within the user communities. Profile information helps us to find the similarity between users. Different similarity measures have been employed to calculate the profile similarity between a connected user pair. Experimental results demonstrate that four basic and easily available features such as workw,educatione,home_townhtandcurrent_city(cc) along with MCC play a vital role in designing a successful classification system for the detection of suspicious links.

A Co-Operative Based Inter-Cluster Communication to Optimize Network Life Time in WSN

Wireless Sensor Networks (WSNs) consist of a large number of low-cost, low-power and intelligent sensor nodes and one or more sinks or base stations. Those nodes are small in size and can perform many important functions, including event sensing, information processing, and data communication. WSNs can be employed in wide military applications and civilian scenarios. Sensing the signal and transmission of those signal require lots of energy which reduces the life time of sensor node, which affects the overall network life time. So for saving the energy a better mechanism is required which can improve the overall network life time. Many research works are going in this field for improving the network life time. For Energy saving a large network is divided in to some cluster from one hop to another hop, where a hop contains a Cluster Head (CH) which is chosen by the Base Station (BS) and a Cluster Member (CM), which can communicate with CH. Clusters, can save the energy due to less distance communication factor. CH communicates with BS. LEACH (Low Energy Adaptive Clustering Hierarchy) is very popular clustering mechanism, but it also has some draw backs. So a better mechanism for energy saving and to improve the life time of network is needed. Here author proposed a new idea called Mutual relay node clustering protocol. Here author analyses the network life time in terms of number of rounds and overall energy consumption and compare with currently existing Leach protocol.

Optimized thermal sensor allocation for field-programmable gate array temperature measurements based on self-heating test

In this paper, an optimization method for ring oscillator (RO)-based thermal sensor allocation based on a fuzzy clustering algorithm is proposed. A sensor array is distributed uniformly on a field programmable gate array (FPGA) as an original layout. Using a trade-off between sensor numbers and measurement resolution, the thermal sensors are combined based on their mutual clustering levels, which are inversely proportional to the temperature difference. This method is suitable for various applications on FPGAs. A self-heating test module that uses the FPGA resources to generate hotspots and large temperature gradients for measurement validation is presented. The experimental results show that when the temperature gradients range from 0°C up to 40°C on an Altera Cyclone IV FPGA, we can reduce sensor resource requirements by 37.5% while maintaining a measurement resolution of 1°C.

Mutual Community Detection across Multiple Partially Aligned Social Networks

Community detection in online social networks has been a hot research topic in recent years. Meanwhile, to enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously, some of which can share common information and structures. Networks that involve some common users are named as multiple aligned In this paper, we want to detect communities of multiple partially aligned networks simultaneously, which is formally defined as the Mutual Clustering problem. The Mutual Clustering problem is very challenging as it has two important issues to address: (1) how to preserve the network characteristics in mutual community detection? and (2) how to utilize the information in other aligned networks to refine and disambiguate the community structures of the shared users? To solve these two challenges, a novel community detection method, MCD (Mutual Community Detector), is proposed in this paper. MCD can detect social community structures of users in multiple partially aligned networks at the same time with full considerations of (1) characteristics of each network, and (2) information of the shared users across aligned networks. Extensive experiments conducted on two real-world partially aligned heterogeneous social networks demonstrate that MCD can solve the Mutual Clustering problem very well.

Characterization of known protein complexes using k-connectivity and other topological measures.

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs.We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms.

Information Fusion and Change Point Detection in Mutual Exclusive Distributive Clustering

Abstract Wireless Sensor Networks in present world are not only aimed for sensing for regular data gathering, can also aimed to extract useful information i.e. Information Fusion. Information Fusion is sometime targeted for change point detection or it can be said for event detection in present scenario. This paper will present information fusion and change point detection merged with our previously proposed work i.e. Mutual Exclusive Distributive Clustering (MEDC). In this paper change point Detection is aimed for forest fire detection via fuzzy logic. MEDC protocol will perform clustering and after that, these cluster heads will use fuzzy logic to represent change point detection. Fire detection is considered here as application for change point detection. In this paper for simulation purpose, data is taken from National Oceanic and Atmospheric Administration [15] and Occupational Safety and health administration [4].

Model-based mutual clustering of flow cytometry data through hierarchical Bayesian modeling

Model-based mutual clustering of flow cytometry data through hierarchical Bayesian modeling

Characterization of known protein complexes using k-connectivity and other topological measures

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs.We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms.

Characterization of known protein complexes using k-connectivity and other topological measures

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs. We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms.

Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA.

Predicting co-complexed protein pairs from heterogeneous data.

Proteins do not carry out their functions alone. Instead, they often act by participating in macromolecular complexes and play different functional roles depending on the other members of the complex. It is therefore interesting to identify co-complex relationships. Although protein complexes can be identified in a high-throughput manner by experimental technologies such as affinity purification coupled with mass spectrometry (APMS), these large-scale datasets often suffer from high false positive and false negative rates. Here, we present a computational method that predicts co-complexed protein pair (CCPP) relationships using kernel methods from heterogeneous data sources. We show that a diffusion kernel based on random walks on the full network topology yields good performance in predicting CCPPs from protein interaction networks. In the setting of direct ranking, a diffusion kernel performs much better than the mutual clustering coefficient. In the setting of SVM classifiers, a diffusion kernel performs much better than a linear kernel. We also show that combination of complementary information improves the performance of our CCPP recognizer. A summation of three diffusion kernels based on two-hybrid, APMS, and genetic interaction networks and three sequence kernels achieves better performance than the sequence kernels or diffusion kernels alone. Inclusion of additional features achieves a still better ROC50 of 0.937. Assuming a negative-to-positive ratio of 600∶1, the final classifier achieves 89.3% coverage at an estimated false discovery rate of 10%. Finally, we applied our prediction method to two recently described APMS datasets. We find that our predicted positives are highly enriched with CCPPs that are identified by both datasets, suggesting that our method successfully identifies true CCPPs. An SVM classifier trained from heterogeneous data sources provides accurate predictions of CCPPs in yeast. This computational method thereby provides an inexpensive method for identifying protein complexes that extends and complements high-throughput experimental data.

Refining intra-protein contact prediction by graph analysis

BackgroundAccurate prediction of intra-protein residue contacts from sequence information will allow the prediction of protein structures. Basic predictions of such specific contacts can be further refined by jointly analyzing predicted contacts, and by adding information on the relative positions of contacts in the protein primary sequence.ResultsWe introduce a method for graph analysis refinement of intra-protein contacts, termed GARP. Our previously presented intra-contact prediction method by means of pair-to-pair substitution matrix (P2PConPred) was used to test the GARP method. In our approach, the top contact predictions obtained by a basic prediction method were used as edges to create a weighted graph. The edges were scored by a mutual clustering coefficient that identifies highly connected graph regions, and by the density of edges between the sequence regions of the edge nodes. A test set of 57 proteins with known structures was used to determine contacts. GARP improves the accuracy of the P2PConPred basic prediction method in whole proteins from 12% to 18%.ConclusionUsing a simple approach we increased the contact prediction accuracy of a basic method by 1.5 times. Our graph approach is simple to implement, can be used with various basic prediction methods, and can provide input for further downstream analyses.

Genomic and gene expression signature of the pre-invasive testicular carcinoma in situ

Testicular cancer is the most common malignancy among men in the reproductive age and the incidence is increasing, probably caused by environmental factors. Most testicular cancers are testicular germ cell tumours and all originate from a carcinoma in situ (CIS) pattern. In this review, we focus on the pre-invasive CIS and its possible fetal origin by reviewing recent data originating from DNA microarrays and comparative genomic hybridisations. A comparison of gene expression and genomic aberrations reveal chromosomal "hot spots" with mutual clustering of gene expression and genomic amplification. Some of the genes found in the hot spots may be involved in creating the CIS phenotype. On the other hand, many genes that are highly expressed in CIS are not present in the hot-spot areas. The gene expression profile of CIS thus most likely reflects the combined result of genomic amplification and increased transcriptional activation and/or deficiency in the epigenetic silencing of specific loci. Amplification of chromosome 12p, appears to be a good genomic marker of the transition from the pre-malignant to malignant CIS cell; this is consistent with recent findings of propagation advantages in cultured undifferentiated embryonic stem cells after spontaneous amplification in similar regions. The gene expression profile of CIS cells has remarkable similarity to that of embryonic stem cells and supports our long-standing hypothesis of an early developmental origin of CIS and testicular germ cell cancer.

Why sort-merge gives the best implementation of the natural join

To join two relations efficiently, they must not only be clustered but mutually clustered . Sorting is the only known way to achieve mutual clustering. Once the relations are sorted, merging is the obvious way to implement the join. If the relations are known to be sorted appropriately, the most costly part of the process can be omitted. To know that a relation is sorted already, it is best to remember that we sorted it. Otherwise detecting that the relation is sorted requires inspection of each tuple, a cost comparable in many cases to the cost of sorting. Detecting any other form of mutual clustering is bound to be a combinatorial process of much greater cost than sorting. Cheaper forms of detection may be possible using sampling, but these are heuristics at best and there is no reliable foundation for them.It is assumed that the sizes of the relations are large compared with the RAM memory available or, equivalently, compared with the number of processors available. Our conclusion that sort-merge is best is thus applicable to single-processor computers or to multiple-processor database machines. Discussing the cost of joining relations without making this assumption is outside the subject of databases.