Hierarchical Fuzzy Clustering Research Articles

Research On Cultivating the Employment Concept of College Students with The Great Spirit of Building the Party - Based on A Data-Driven Approach

One of the most essential ways to see the worth of college students is via employment, which is also the main focus of student development programs for the Spirit of Building the Party. Some real-world issues with the conventional approach to college career counselling stem from the disconnection between students' education and the real world. For example, there is often a misalignment between students' social needs and their comprehensive skill sets and between students' job-seeking needs and their actual employment situations. There has to be extensive research into how to better assist college graduates in finding work that suits their skills and experience so that society can fulfil the practical demands of its growth in this area for the Spirit of Building the Party. Problems in the workplace have garnered a lot of attention because they threaten society's steady progress and the quality of life for its citizens. The nation now needs to tackle the issue of challenging employment for the Spirit of Building the Party. Institutions of higher learning have responded to societal and economic demands by emphasizing the development of students' social abilities and increasing their access to the job market. To provide a solid groundwork for creating employment assistance programs, this research suggests an analytical strategy for college students' jobs based on a data-driven approach for the Spirit of Building the Party. The proposed model is used to plan the supply and structure of skills from a talent supply standpoint. In contrast, the regression model forecasts university students' employment requirements for the Spirit of Building the Party. The data mining technique used in this work combines enhanced fuzzy hierarchical clustering and feature extraction based on semantic similarity correlation. The testing findings showed that the method performed better in categorizing data and could easily handle and analyze big-text datasets

Two-stage fusion strategy and residual shrinkage capsule network for SAR image change detection

ABSTRACT Acquiring precise change map for synthetic aperture radar (SAR) images poses challenges due to the absence of labelled datasets and the presence of speckle noise interference. This paper presents a two-stage fusion strategy coupled with capsule network classification detection method. Initially, to enhance the structure of change areas within the difference image (DI), we propose an energy ratio (ER) algorithm based on the mean ratio (MR). Subsequently, employing a two-stage fusion strategy integrating multiplication and discrete wavelet transform (DWT), we fuse the DIs generated by the three algorithms to effectively complement change information across distinct DIs. In addition, a saliency detection algorithm is introduced to mitigate noise interference and balance the number of samples. Hierarchical fuzzy c-means clustering (HFCM) is then used to pre-classify the salient fusion difference image (SFDI) and derive pseudo-labels. Finally, we apply the residual shrinkage capsule network (RSCN) to reclassify pre-classification results and obtain the change detection outcome, capable of extracting the multi-scale refinement features and adaptively learning discriminant information from changed and unchanged regions. Experimental evaluations on various SAR image datasets demonstrate the exceptional detection performance of the proposed method.

Intelligent analysis system of college students' employment and entrepreneurship situation: Big data and artificial intelligence-driven approach

This study proposes an analysis plan for college students' employment and entrepreneurship based on big data and artificial intelligence, aiming to provide an important basis for the development of employment guidance activities. To realize feature analysis and useful information extraction of large-scale text information data, this study adopts a big data mining algorithm based on fuzzy hierarchical clustering analysis and semantic similarity correlation feature extraction. The algorithm uses generalization mapping to construct a semantic concept tree, and a fuzzy analytic hierarchy process to judge the semantic similarity and relevance of big data. This study demonstrates the efficacy of a big data mining algorithm through experimental verification. Two public datasets were used to validate the proposed method. The experimental results revealed that the method could efficiently process and analyze large textual datasets, leading to superior data classification performance.

Change Detection in SAR Images through Clustering Fusion Algorithm and Deep Neural Networks

The detection of changes in synthetic aperture radar (SAR) images based on deep learning has been widely used in landslides detection, flood disaster monitoring, and other fields of change detection due to its high classification accuracy. However, the inherent speckle noise in SAR images restricts the performance of existing SAR image change detection algorithms by clustering analysis. Therefore, this paper proposes a novel method for SAR image change detection based on clustering fusion and deep neural networks. We first used hierarchical fuzzy c-means clustering (HFCM ) to process two different images to obtain HFCM classification results. Then a fusion strategy is designed to obtain the fused image from the two HFCM classified images as the pre-classification result. Furthermore, a lightweight deep neural network com posed of a decomposition convolution module and an auxiliary classification module was proposed; the former module could reduce network parameters by 28%, and the latter could reduce network parameters by 33.3%. To improve the recognition performance of the network, the classification layer was replaced by the regression layer at the outcome of the network. By comparing the experiments of different methods on five data sets, the performance of our proposed method is superior.

FHC-NDS: Fuzzy Hierarchical Clustering of Multiple Nominal Data Streams

The need of fuzzy clustering arises in many real-world applications such as clumping the users based on their web browsing behavior where the behavior of a user can be similar to two different sets of users at the same instance. The aptness of fuzzy clustering for data streams is further intensified given their concept evolving nature. Data streams can be clustered either by following clustering-by-variable approach or clustering-by-example approach. Most of the existing fuzzy clustering-by-variable methods are applicable to numeric data streams only. In this article, a fuzzy hierarchical clustering method is proposed for clustering multiple nominal data streams using clustering-by-variable approach. The fuzzy affinity of data streams to different clusters is calculated using normalized cosine similarity to the cluster centroids. It handles the concept evolution by updating the hierarchical clustering structure by either merging and/or splitting the nodes depending on the extent to which the node entropy changes. The performance of the proposed method is analyzed and compared to hierarchical clustering for multiple nominal data streams (HCND), semifuzzy online divisive-agglomerative clustering, and nTreeClus on synthetic as well as real-world web-browsing dataset where it has outperformed all three in terms of cluster quality as quantified by Dunn index, modified Hubert <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Gamma$</tex-math></inline-formula> statistic, and adjusted rand index. Furthermore, the experimental results show that the proposed method is highly promising with regard to capturing fuzzy clusters as indicated by Xie-Beni index, partition coefficient, and partition entropy.

Correlation between Chemical Profile of Georgian Propolis Extracts and Their Activity against Helicobacter pylori.

Helicobacter pylori (H. pylori) is considered the most common bacterial pathogen colonizing stomach mucosa of almost half the world's population and is associated with various gastrointestinal diseases (from digestive problems and ulcers to gastric cancer). A lack of new drugs and a growing number of H. pylori antibiotic-resistant strains is a serious therapeutic problem.As a mixture of natural compounds, propolis has antimicrobial activity based on high concentrations of bioactive polyphenols (mainly flavonoids and phenolic acid derivates). The chemical composition of tested Georgian propolis is characterized by the presence of flavonoids aglycones, and phenolic acid monoesters, e.g., pinobanksin-5-methyl ether, pinobanksin, chrysin, pinocembrin, galangin, pinobanksin-3-O-acetate, pinostrobin and pinobanksin-3-O-butanoate, or isobutanoate and methoxycinnamic acid cinnamyl ester. The anti-H. pylori activity of 70% ethanol water extracts of 10 Georgian propolis samples was evaluated in vitro by MIC (minimal inhibitory concentration) against the reference strain (H. pylori ATCC 43504) and 10 clinical strains with different antibiotic-resistance patterns. The strongest anti-Helicobacter activity (MIC and MBC = 31.3 µg/mL) was observed for propolis from Orgora, Ota, and Vardzia and two from Khaheti. Lower levels of activity (MIC = 62.5 µg/mL) were found in propolis obtained from Qvakhreli and Pasanauri, while the lowest effect was observed for Norio and Mestia (MIC = 125.0 µg/mL). However, despite differences in MIC, all evaluated samples exhibited bactericidal activity. We selected the most active propolis samples for assessment of urease inhibition property. Enzyme activity was inhibited by propolis extracts, with IC50 ranging from 4.01 to 1484.8 µg/mL. Principal component analysis (PCA) and hierarchical fuzzy clustering (dendrograms) coupled with matrix correlation analysis exhibited that the strongest anti-Helicobacter activity was connected with black poplar origin and high flavonoid content of propolis. Samples with lower activity contained higher presence of aspen markers and/or dominance of non-flavonoid polyphenols over flavonoids. In summary, Georgian propolis can be regarded as a source bioactive compounds that can be used as adjuvant in therapy of H. pylori infection.

Contactless medical equipment AI big data risk control and quasi thinking iterative planning

Research Background, the intelligent polymorphic system of heavy core clustering fitting iterative programming is constructed by using the edge lens of dual core heavy core. The tracking system of heavy core TANH equilibrium array is used to obtain the abnormal data range. The energy regular fluctuation of the edge lens with dual core and heavy core is used to obtain high-definition images. And build the complexity dependent parameter group from low-end equipment to high-end equipment. Heavy core clustering of hierarchical fuzzy clustering system based on differential incremental balance theory is applied to Contactless medical equipment AI big data risk control and quasi thinking iterative planning. At the same time, the mathematical model risk control is performed by fitting the TANH balance of the local nonlinear random regular micro-vibration diffusion curve. The CT/MR original data is subjected to hierarchical cross domain overlapping grid screening with the structure of fitting weakly nonlinear curve, which can capture the heavy core cluster analysis of the core layer of big data anomalies [1:10]. Successfully control the parameter group of CT/MR machine internal data, big data AI Mathematical model risk. The polar graph of high-dimensional heavy core clustering processing data is regular and scientific. The same time, it can prevent the dimension disaster caused by the construction of high-dimensional big data due to the partial loss of original data, and form a stable and predictable maintenance of CT/MR. Compared with the discrete characteristics of the polar graph of the original data. So as to correctly detect and control the dynamic change process of CT/MR in the entire life cycle. It provides help for predictive maintenance of early pre-inspection and orderly maintenance of the medical system, and developed standardized model software of automated unsupervised learning for medical big equipment big data AI Mathematical model risk control. Scientifically evaluated the exposure time and heat capacity MHU% of CT tubes, as well as the internal law of MR (nuclear magnetic resonance), and processed big data twice and three times in heavy nuclear clustering. After optimizing the algorithm, hundreds of thousands of nonlinear random vibrations are performed in the operation and maintenance database every second, and at least 30 concurrent operations are formed, which greatly improves and shortens the operation time (Yanwei et al. in J Complex 2017:1–9, 2017. https://doi.org/10.1155/2017/3437854). Finally, after adding micro-vibration quasi thinking iterative planning for the uncertain structure of AI operation, we can successfully obtain the scientific and correct results required by high-dimensional information and analyze images. This kind of AI big data risk control improves the intelligent management ability of medical institutions. Cross platform embedded web system for predictable maintenance of AI big data is established (Qi et al. in J IEEE Trans Ind Inf 99:1, 2020. https://doi.org/10.1109/tii.2020.3012157).

Behavior Prediction Scheme Using Hierarchical Clustering and Deep Neural Networks

Nowadays, most companies are utilizing customer behavior mining frameworks to improve their business strategies. These frameworks are used to predict different business patterns, such as sales, forecasting, or marketing. Different data mining and machine learning concepts have been applied to predict customer behaviors. However, traditional approaches consume more time and fail to predict exact user behaviors. In this paper, intelligent techniques, such as fuzzy clustering and deep learning approaches, are utilized to investigate customer portfolios to detect customers’ purchasing patterns. To accomplish this objective, hierarchical fuzzy clustering was applied to compute the relationship between products and purchasing criteria. According to the analysis, similar data are grouped together, which reduces the maximum error classification problem. Then, an optimized deep recurrent neural network is incorporated into this process to improve the prediction rate. The discussed system efficiency is evaluated using a number of datasets with respective performance metrics. The proposed approach was compared to other single model-based and hybrid model-based approaches and was found to attain maximum accuracy and minimum error rate in comparison.

PIFHC: The Probabilistic Intuitionistic Fuzzy Hierarchical Clustering Algorithm

Hierarchical clustering techniques help in building a tree-like structure called dendrogram from the data points which can be used to find the closest related data objects. This paper presents a novel hierarchical clustering technique which considers intuitionistic fuzzy sets to deal with the uncertainty present in the data. Instead of using traditional hamming distance or Euclidean distance measure to find the distance between the data points, it employs the probabilistic Euclidean distance measure to propose a novel clustering approach which we term as ‘Probabilistic Intuitionistic Fuzzy Hierarchical Clustering (PIFHC) Algorithm’. The proposed PIFHC algorithm considers probabilistic weights from the data to measure the distances between the data points. Clustering results over UCI datasets show that our proposed PIFHC algorithm gives better cluster accuracies than its existing counterparts. PIFHC efficiently provides improvements of 1%–3.5% in the clustering accuracy compared to other fuzzy hierarchical clustering algorithms for most of the datasets. We further provide experimental results with the real-world car dataset and the Listeria monocytogenes dataset for mouse susceptibility to demonstrate the practical efficacy of the proposed algorithm. For Listeria datasets as well, proposed PIFHC records 1.7% improvement against the state-of-the-art methods The dendrograms formed by the proposed PIFHC algorithm exhibits high cophenetic correlation coefficient with an improvement of 0.75% over others. We provide various AGNES methods to update the distance between merged clusters in the proposed PIFHC algorithm.

The Antimicrobial Properties of Poplar and Aspen-Poplar Propolises and Their Active Components against Selected Microorganisms, including Helicobacter pylori.

There is a noticeable interest in alternative therapies where the outcome is the eradication of the Gram-negative bacterium, Helicobacter pylori (H. pylori), for the purpose of treating many stomach diseases (chronic gastritis and peptic ulcers) and preventing stomach cancer. It is especially urgent because the mentioned pathogen infects over 50% of the world’s population. Recent studies have shown the potential of natural products, such as medicinal plant and bee products, on the inhibition of H. pylori growth. Propolis is such a bee product, with known antimicrobial activities. The main scope of the study is the determination of the antimicrobial activity of ethanolic extracts from 11 propolis samples (mostly from Poland, Ukraine, Kazakhstan, and Greece) against H. pylori, as well as selected bacterial and yeast species. The most effective against H. pylori was the propolis from Ukraine, with an MIC = 0.02 mg/mL while the rest of samples (except one) had an MIC = 0.03 mg/mL. Moreover, significant antimicrobial activity against Gram+ bacteria (with an MIC of 0.02–2.50 mg/mL) and three yeasts (with an MIC of 0.04–0.63 mg/mL) was also observed. A phytochemical analysis (polyphenolic profile) of the propolis samples, by ultra-high-performance liquid chromatography-diode array detector-mass spectrometry (UPLC-DAD-MS), was performed. An evaluation of the impact of the propolis components on antimicrobial activity, consisting of statistical analyses (principal component analysis (PCA) and hierarchical fuzzy clustering), was then performed. It was observed that the chemical composition characteristics of the poplar propolis correlated with higher antibacterial activity, while that of the poplar and aspen propolis correlated with weaker antibacterial activity. To summarize the activity in vitro, all tested propolis samples indicate that they can be regarded as useful and potent factors in antimicrobial therapies, especially against H. pylori.

Fuzzy Clustering in Assortative and Disassortative Networks

Despite the significance of assortativity as a property of networks that paves for the emergence of new structural types, surprisingly, there has been little research done on assortativity. Assortative networks are perhaps among the most prominent examples of complex networks believed to be governed by common phenomena, thereby producing structures far from random. Further, certain vertices possess high centrality and can be regarded as significant and influential vertices that can become cluster centers that connect with high membership to many of the surrounding vertices. We propose a fuzzy clustering method to meaningfully characterize assortative, as well as disassortative, networks by adapting the Bonacichi’s power centrality to seek the high degree centrality vertices to become cluster centers. Moreover, we leverage our novel modularity function to determine the optimal number of clusters, as well as the optimal membership among clusters. However, due to the difficulty of finding real-world assortative network datasets that come with ground truths, we evaluated our method using synthetic data but possibly bearing resemblance to real-world network datasets as they were generated by the Lancichinetti–Fortunato–Radicchi benchmark. Our results show our non-hierarchical method outperforms a known hierarchical fuzzy clustering method, and also performs better than a well-known membership-based modularity function. Our method proved to perform beyond satisfactory for both assortative and disassortative networks.

Improved Fuzzy Hierarchical Clustering Algorithm and its Application in Software Re-architecting

Computer technology changes with each passing day. As a member of software system, the architecture of legacy system becomes complex and difficult to adapt to complex software functional requirements in the process of evolution. It has not only affected the late legacy system maintenance work, but also to the enterprise has brought serious losses. In order to solve the above problems, this paper presents a Fuzzy Hierarchical Clustering algorithm Based on Information Loss, FHCBIL. There are two improvements in FHCBIL algorithm: On the one hand, this algorithm extends the weights allocation of entity features, which solves the problem that the two element relation can not reflect the influence degree of entity feature on entity. On the other hand, FHCBIL algorithm uses the information loss as a similarity calculation method, avoiding the problem of sensitive data set to the irregular data set in the clustering process. In this paper, we further apply FHCBIL algorithm in software re-architecting, give the model of software re-architecting, and implement new nested software architecture. In order to evaluate the quality of the new architecture, we evaluate the new architecture from two aspects: the rationality of the new architecture hierarchy and the accuracy of entity division. The experimental results show that the new architecture based on FHCBIL algorithm has high cohesion and low coupling. And the accuracy of the new architecture is high.

Data integration by fuzzy similarity-based hierarchical clustering

BackgroundHigh throughput methods, in biological and biomedical fields, acquire a large number of molecular parameters or omics data by a single experiment. Combining these omics data can significantly increase the capability for recovering fine-tuned structures or reducing the effects of experimental and biological noise in data.ResultsIn this work we propose a multi-view integration methodology (named FH-Clust) for identifying patient subgroups from different omics information (e.g., Gene Expression, Mirna Expression, Methylation). In particular, hierarchical structures of patient data are obtained in each omic (or view) and finally their topologies are merged by consensus matrix. One of the main aspects of this methodology, is the use of a measure of dissimilarity between sets of observations, by using an appropriate metric. For each view, a dendrogram is obtained by using a hierarchical clustering based on a fuzzy equivalence relation with Łukasiewicz valued fuzzy similarity. Finally, a consensus matrix, that is a representative information of all dendrograms, is formed by combining multiple hierarchical agglomerations by an approach based on transitive consensus matrix construction. Several experiments and comparisons are made on real data (e.g., Glioblastoma, Prostate Cancer) to assess the proposed approach.ConclusionsFuzzy logic allows us to introduce more flexible data agglomeration techniques. From the analysis of scientific literature, it appears to be the first time that a model based on fuzzy logic is used for the agglomeration of multi-omic data. The results suggest that FH-Clust provides better prognostic value and clinical significance compared to the analysis of single-omic data alone and it is very competitive with respect to other techniques from literature.

Neutrosophic Fuzzy Hierarchical Clustering for Dengue Analysis in Sri Lanka

In the structure of nature, we believe that there is an underlying knowledge in all the phenomena we wish to understand. Mainly in the area of epidemiology we often tend to seek the structure of the data obtained, pattern of the disease, nature or cause ofits emergence among living organisms. Sometimes, we could see the outbreak of disease is ambiguous and the exact cause of the disease is unknown. A significant number of algorithms and methods are available for clustering disease data. We could see that literature has no traces of including indeterminacy or vagueness in data which has to be much concentrated in epidemiological field. This study analyzesthe attack of dengue in 26 districts of Sri Lanka for the period of seven years from 2012 to 2018. Clusters with low risk, medium risk and high risk areas affected by dengue are identified. In this paper, we propose a new algorithm called Neutrosophic-Fuzzy Hierarchical Clustering algorithm(NFHC) that includes indeterminacy. Proposed algorithm is compared with fuzzy hierarchical clustering algorithm and hierarchical clustering algorithm. Finally the results are evaluated with the benchmarking indexes and the performance of the clustering algorithm is studied. NFHC has performed a way better than the other two algorithms.

A thumbnail-based hierarchical fuzzy clustering algorithm for SAR image segmentation

This paper proposes a novel algorithm for segmentation of synthetic aperture radar (SAR) image, our proposed algorithm (THFCM) is based on thumbnail representations and a hierarchical fuzzy C-means (FCM) approach. THFCM firstly divides the image into pixel groups to extract local feature, and the major pixels of each pixel group are selected to construct a thumbnail. FCM is then used to segment each thumbnail, and hierarchical segmentation is then performed on the overall image data, based on the results of thumbnail clustering. The thumbnail approach leverages local image information, helping to overcome speckle noise, while the hierarchical approach improves computational efficiency. Experiments on simulated and real SAR images suggest that THFCM outperforms several other state-of-the-art algorithms in terms of both segmentation accuracy and running time.

A Clinical Support System for Brain Tumor Classification Using Soft Computing Techniques.

A brain tumor is an accumulation of abnormal cells in human brain. As tumor increases in size, it induces brain damage. Hence it is essential to diagnose the type of brain tumor. The effective modality used for brain tumor diagnose is MRI because of its remarkable image resolution, the speed of acquisition, and high safety profile for patients. The analysis of brain MRI is an important part of patient care and decision. Hence in the proposed Clinical Support System, the brain MRI image is preprocessed using Genetic Optimized Median Filter followed by brain tumor region segmentation using Hierarchical Fuzzy Clustering Algorithm. The features of the tumor region are extracted through GLCM feature extraction method. Lion Optimized Boosting Support Vector machine model is used for further classification of tumor by Brain Tumor Image Segmentation (BraTS) dataset. Hence the proposed clinical support system provides an integrated model for Detection and classification of brain tumor which assists the doctors in appropriate evaluation of tumor.

Customer-Oriented Vehicle Routing Problem with Environment Consideration: Two-Phase Optimization Approach and Heuristic Solution

With the fastest consumer demand growth, the increasing customer’s demands trend to multivarieties and small-batch and the customer requires an efficient distribution planning. How to plan the vehicle route to meet customer satisfaction of mass distribution as well as reduce the fuel consumption and emission has become a hot topic. This paper proposes a two-phase optimization method to handle the vehicle routing problem, considering the customer demands and time windows coupled with multivehicles. The first phase of the optimization method provides a fuzzy hierarchical clustering method for customer grouping. The second phase formulates the optimization en-group vehicle routing problem model and a genetic algorithm to account for vehicle routing optimization within each group so that fuel consumption and emissions are minimized. Finally, we provide some numerical examples. Results show that the two-phase optimization method and the designed algorithm are efficient.

GROUNDWATER CHARACTERIZATION IN SOUTHWESTERN ROMANIA USING FUZZY HIERARCHICAL CROSS CLUSTERING

A simultaneous hierarchical classification of groundwater in the Banat Plain-southwestern Romania and associated characteristics such as As(III), As(V), total As, trace metals (Al, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Zn), pH and redox potential using Fuzzy Hierarchical Cross Clustering Algorithm was carried out. In samples containing more than 10 g/L total As (13.9 77.3 g/L), the As(V) species represented 57 20% and 73 18% in the others. Arsenic speciation pattern was in agreement with basic pH (8.31 8.49) and oxidizing conditions (369.0 409.1 mV) in aquifer. The contents of metals were below the threshold values excepting Mn. The Fuzzy Hierarchical Cross Clustering Algorithm emphasized pH and redox potential as main parameters responsible for water samples grouping. Part of water samples containing > 10 g/L As have as associated parameters the content of Pb, Cr, Zn and Fe, and pH plays a key role in natural occurrence of these elements by desorption from Fe minerals/oxyhydroxides. The other water samples with > 10 g/L As were grouped based on redox potential, As (III), As (V), total As, Cd, Ni, Cu, Al and Mn concentrations. The source of these elements was attributed to desorption processes from Al minerals/oxyhydroxides and Mn oxyhydroxides governed by the redox potential. The Fuzzy Hierarchical Cross Clustering emphasized the importance of trace metals quantification for classification of groundwater with naturally enriched As content.

Abstract 1555: A gene signature associated with PTEN activation defines good outcomes in intermediate-risk prostate cancer cases

Abstract Background: Accurate identification of Gleason score (GS) 3+4 prostate cancer patients with low risk of disease progression is an unmet challenge in treatment decision making. We investigated the potential of using integrative genomic profiling to improve stratification of GS 3+4 prostate cancer patients to aid decision making. Methods: High-throughput gene expression, methylation and mutational analysis were performed on a subset of 28 GS 3+4 cases selected from a cohort (N = 69) of intermediate risk prostate cancer cases. Preselection was based on immunohistochemistry (IHC) expression of 6 major drivers (PTEN, MYC, RB1, TP53, AURKA and AR) of aggressive disease in prostate cancer. Fuzzy clustering and unsupervised hierarchical clustering were utilized to determine molecular subgroups. Genes with high expression differences between subgroups were identified through group difference statistics. A prognostic index based on Cox proportional hazard model was generated for individual and combinations of differentially expressed genes and was tested in GS 3+4 cases (total N = 215, median follow-up = 65.8 months) extracted from 3 prostate cancer datasets available at Gene Expression Omnibus (GSE21032, GSE16560 and GSE40272). Hazard risk (HR) and 95% confidence interval (CI) of association with outcomes (recurrence and overall survival) were analyzed using Cox modelling and log-rank analysis. Results: We found that GS 3+4 prostate cancer cases could be classified as "complex" (differential expression of more than one driver) or "simple" (differential expression of only one) based on IHC analysis. Focusing on the "simple" cases, gene expression and methylation PTEN expression were tightly correlated and not observed in other drivers (R = 0.41, P = 0.03). Group difference statistics showed 35 genes were highly expressed in PTEN-high subgroup and were evaluated individually and in combination for prognostic value in 3 independent cohorts. The prognostic potential of the combinatorial signature was observed in all 3 cohorts: GSE21032 (HR: 6.95, 95% CI: 2.73-17.54, P &amp;lt; 0.001), GSE40272 (HR: 6.40, 95% CI: 2.28-17.96, P &amp;lt; 0.001) and GSE16560 (HR: 1.77, 95% CI, 1.29-2.41; P = 0.003) datasets. Individual gene-by-gene analysis showed that 4 genes (ACTA2, ACTG2, MYH11 and TPM2) were prognostic (all P &amp;lt; 0.05) in all 3 cohorts. Compared to the combination of 35 genes (AUC = 0.728), the prognostic potential of combining these 4 genes was still of significant value (AUC = 0.689). Conclusion: This study shows that by using IHC as an upfront stratifier of intermediate risk prostate cancers, it is possible to identify, through subsequent differential gene expression profiling, a geneset with prognostic power across multiple cohorts. This strategy has not been used previously and the signature, subject to further validation, has the potential to impact on treatment decisions in GS 3+4 prostate cancer patients. Citation Format: Chee Wee Ong, Pamela Maxwell, Muhammad A. Alvi, Stephen McQuaid, Ian G. Mills, Manuel Salto-Tellez, David J. Waugh. A gene signature associated with PTEN activation defines good outcomes in intermediate-risk prostate cancer cases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1555.

Optimal Sentence Clustering for Web Database using Hierarchical Fuzzy Relational Clustering Integrated with Artificial Bee Colony Algorithm

Sentence clustering plays a vital role in text mining and text processing activities. The proposed work is a novel hierarchical fuzzy relational clustering algorithm (HFRECA) capable of identifying sub clusters. It has features of both hierarchical clustering and fuzzy clustering in which it uses page rank to form multiple clusters present in text documents containing hierarchical structure. To enhance the quality of the clusters formed, an optimisation algorithm which is called artificial bee colony (ABC) algorithm is used along with it. The proposed algorithm identifies the sub clusters and finely tunes the cluster to show a better optimised result.