Similarity and cluster analysis algorithms for Microarrays using R trees

Image content analysis plays an important role for adaptive multimedia retrieval. In this chapter, the authors present their work on using a useful spatial data structure, R*-tree, for similarity analysis and cluster analysis of image contents. First, they describe an R*-tree based similarity analysis tool for similarity retrieval of images. They then move on to discuss R*-tree based clustering methods for images, which has been a tricky issue: although objects stored in the same R* tree leaf node enjoys spatial proximity, it is well-known that R* trees cannot be used directly for cluster analysis. Nevertheless, R* tree’s indexing feature can be used to assist existing cluster analysis methods, thus enhancing their performance of cluster quality. In this chapter, the authors report their progress of using R* trees to improve well-known K-means and hierarchical clustering methods. Based on R*-Tree’s feature of indexing Minimum Bounding Box (MBB) according to spatial proximity, the authors extend R*-Tree’s application to cluster analysis containing image data. Two improved algorithms, KMeans-R and Hierarchy-R, are proposed. Experiments have shown that KMeans-R and Hierarchy-R have achieved better clustering quality.

An air combat maneuver pattern extraction based on time series segmentation and clustering analysis

Purpose:Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET‐imaging features, assessed with various clustering algorithms.Methods:[18F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in‐house segmentation algorithm combining gradient and region‐growing techniques; resulting ROIs were used to extract 54 PET‐based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC‐associated genes‐ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K‐means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets.Results:Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JImean= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full‐gene set (JI =1) for all values of K, and the agreement between the GOI set and the full‐gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K‐means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JIrange: 0.2301–1).Conclusion:Using commonly‐used clustering algorithms, we found poor agreement in patient characterization from genomic versus imaging data. Interpretation of radiogenomic associations was found to be dependent on clustering algorithms and sensitive to data reduction techniques.

Cycads are currently suffering significant habitat loss from deforestation, agricultural expansion, and illegal harvesting. Dioon purpursii Rose has restricted distribution and small populations, which until now are endemic in the state of Oaxaca in Mexico, and it has been included in the category of threatened and vulnerable. Three sites with D. purpusii plants were located in the state of Oaxaca. Structural and dendrometric characteristics of the population and physiognomic, floristic and environmental characteristics of the forest community at each site were determined. Dendrometric variables were evaluated by an analysis of variance (ANOVA), and a similarity analysis between sites was applied to all characteristics, followed by a cluster analysis to express the relationships between sites. The population structure of the three sites is typical of species in which a great frequency of seedlings and juveniles suggests optimal microhabitat conditions for initial establishment. The results of the ANOVA showed that the most developed plants were found at Site 3, followed by Sites 1 and 2. The floristic composition corresponded to the type of tropical deciduous forest vegetation in the region, where D. purpusii formed part of the tree, shrub and herb layers. The phenogram resulting from the similarity and cluster analyses showed that Sites 1 and 2 are more similar (0.97), and Site 3 less similar to the other two (0.84). Although the population structure of D. purpusii at the three sites is similar to that found for other cycad species, the main threats are the small number of reproductive individuals, harvesting of leaves, and the constant threat of habitat alteration, like construction of roads, contributing to extinction.

Crowd sensing between users with smart mobile devices is a new trend of development in Internet. In order to recommend the suitable service providers for crowd sensing requests, this paper presents a Fine-grained Crowdsourcing Model (FCM) based on Ontology theory that helps users to select appropriate service providers. First, the characteristic properties which extracted from the service request will be compared with the service provider based on ontology triple. Second, recommendation index of each service provider is calculated through similarity analysis and cluster analysis. Finally, the service decision tree is proposed to predict and recommend appropriate candidate users to participate in crowd sensing service. Experimental results show that this method provides more accurate recommendation than present recommendation systems and consumes less time to find the service provider through clustering algorithm.

Tadlac Lake was heavily used for aquaculture until the late 1990s which resulted in a deterioration of its water quality. Aquaculture operations were banned to rehabilitate the lake after a massive fish kill in 1999. However, no thorough assessment of the rehabilitation's effectiveness has been conducted. In order to assess the success of the rehabilitation efforts, this study evaluated the lake's trophic status through study of its zooplankton community and physical-chemical parameters. Zooplankton samples and physical-chemical parameters were collected once a month from October 2017 to March 2018. Overall, 25 zooplankton species were documented during the sampling period. Rotifera dominated the zooplankton community with a relative density of 46.19%, followed by Copepoda (36.70%) and Cladocera (17.11%). Eutrophic indicator species Brachionus forficula had the highest density among the zooplankton taxa, followed by Keratella tropica and Brachionus havanaensis. Significant variation in zooplankton density was documented over the sampling period (p<0.05). Cluster analysis and analysis of similarities (ANOSIM) showed variation in the zooplankton density across the sampling months. Similarity percentage (SIMPER) revealed that the differences in plankton density were due to the densities of the most abundant taxa. Canonical correspondence analysis (CCA) showed that the zooplankton community was highly influenced by dissolved oxygen, conductivity, pH, biological oxygen demand, and temperature. The high density of the eutrophic indicator zooplankton species and the nutrient concentration of the lake indicated that Tadlac lake was still under eutrophic conditions. Biotic indices further confirmed that the lake was experiencing eutrophic conditions with moderate organic pollution. Therefore, additional approaches are necessary to effectively control the nutrient enrichment in the lake to improve its condition.

Buntonia is a common genus in the Maastrichtian to Late Eocene basins of Africa. Analysis of similarity (ANOSIM), hierarchical clustering (HC), and non-metric multidimensional scaling (nMDS) were used to analyze the geographic pattern of Buntonia species based on the presence/absence matrix of 42 species. The Jaccard-based HC shows that the African countries can be subdivided into three main bioregions: North Africa (Algeria, Libya, Tunisia, Morocco, and Sudan), West Africa (Mali, Togo, Benin, Cote d’Ivoire, Senegal, Ghana, Gambia, Nigeria, Cameron, and Mauritania), and South Africa (Namibia and South Africa). ANOSIM indicated a non-significant difference between North and West African countries and between West and Southern Africa. However, a significant difference was found between North and Southern Africa (sequential Bonferroni corrected p < 0.01). These patterns are well-characterized also by both of HC and nMDS. Moreover, analyses of the stratigraphic ranges of the Buntonia species revealed a distinct range shift within the regions of the Guinean Gulf (Togo, Benin, and Cote d’Ivoire) through Senegal towards North Africa. The latter indicates two possible migration routes from West Africa to North Africa through the Trans-Saharan seaway and from the Atlantic to the Tethyan Sea. Migration from region to region played an important role in the distribution of the studied Buntonia species rather than origination of new species and/or extinction of existing species.

Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE's potential in time series clustering anticipated in future research.

Combining hierarchical clustering approaches using the PCA method

Clustering is an effective unsupervised machine learning method that can be used as a stand-alone heuristic or as a part of a data mining process. The goal of clustering analysis is to partition data into groups with high intra-cluster association, and low inter-cluster association. Hierarchical clustering requires minimal parameters, has flexibility with similarity measure, and has strong visualization power, all of which makes it ideal for exploratory analysis. Hierarchical clustering is a particular branch of clustering algorithms where the results are not given as partitions, but rather a nested structure, which can represent the ordering among elements within a dataset. The study of the performance of hierarchical structure on time series data is limited. In this paper, we examine the hierarchical structure of time series datasets. The most popular hierarchical structured clustering heuristics include the Hierarchical Agglomerative Clustering, which is distance based; and Ordering Points To Identify the Clustering Structure, which is density based. Both share many similar characteristics and are suitable for time series data processing. We examine the performance of different hierarchical clustering algorithms with time series both internally as well as externally.

An experiment was conducted to evaluate 49 spring wheat (Triticum aestivum L.) &nbsp;genotypes of diverse origin by estimating genetic parameters viz. variability, character association, path coefficient, cluster and principle component analysis (PCA) for yield and spot blotch disease resistance during 2011 -2012 and 2012 - 2013. Highest phenotypic coefficient of variation (PCV) was observed for area under disease progress curve (AUDPC) (29.15%), plot yield (12.94%) and 1000-kernel weight (11.63%). The highest plot yield (g) was observed in genotypes WH1132 and WH 1131. Grain yield per plot (g) was significantly and positively associated with the 1000-kernel weight (g) (0.82*) and grain per spike (number) (0.79*). Path-coefficient analysis expressed that the maximum positive direct effect on yield showed by grain per spike (number) observed via 1000-kernel weight (g) and days to 75% flowering (days) while negative direct effects showed by 1000-kernel weight (g), AUDPC, days to maturity (days) and plant height (cm). All the 49 spring wheat genotypes were grouped into six distinct clusters. The genotypes of cluster II represented higher yield and disease resistance potential. Out of the major four principal components (PCs), three principal components (PC1, PC2 and PC3) accounted for 79.86% with proportionate values of 45.90, 18.73 and 15.23%, respectively. The third principal component has high positive component value for the days to 75% flowering, the plant height, the AUDPC and the 1000-kernel weight. The breeding objective of the present experiment is to identify genetically diverse wheat genotypes for developing high yielding and disease resistant variety for Eastern Gangetic Plains of India. &nbsp; &nbsp; Key words: AUDPC, cluster analysis, dendrogram, genetic advance, yield, PCA and PCV.

In order to establish HPLC fingerprint of Liuwei Dihuang soft capsule, and to provide certain reference for an quality control of it, the HPLC method was performed on an Agilent C18 (4.6 mm x 250 mm, 5 μm) column with acetonitrile-0.02% trifluoroacetic acid as mobile phase, gradient elution volume flow of 1.0 mL x min(-1), column temperature was 30 degrees C, detection wavelength: 0-60 min, 238 nm, 60-70 min, 210 nm. The software for chromatographic fingerprint was applied to analysis different batches of Liuwei Dihuang soft capsule samples. Sixteen mutual peaks were selected as the fingerprint peaks in 12 samples with loganin as the reference peak, and all of the detected peaks were separated effectively. Cluster analysis (HCA) and similarity analysis (SA) were done based on data of 12 samples clustering analysis of 12 batches of samples were divided into 2 categories. Including 7 for the first class, the rest was second, similarities calculated by SA were all above 0.92, indicating a good similarity between the reference and twelve batches of samples, also, the analysis results of HCA and SA basically the same. This method is simple with good precision, repeatability and stability, and provides the basis for Liuwei Dihuang soft capsule quality control.

This paper focuses on an approach to address large-scale data gathered from heterogeneous sources by integrating static and dynamic data in hierarchical clusterization, and its application to the analysis of retail branches. Traditionally, branch clustering analysis has relied on static information and the utilization of statistical measures to extract relevant features from the dynamic data and incorporate them into the static dataset; however, the application of this approach presents several challenges. This research proposes a solution that addresses these disadvantages while aiming to maintain the success achieved when applying unsupervised machine learning algorithms. The paper presents an approach based on the integration of static attributes and time series data in a hierarchical clustering manner that enables the identification of key performance indicators and offers insight into factors that influence branch performance over time. The results show the potential to optimize resource allocation, inventory management, and customer service strategies. The proposed approach is demonstrated using retail shop data from a Spanish telecommunications company (Grupo Masmovil), highlighting its effectiveness in enhancing cluster profiling and offering meaningful insights beyond the prevailing approaches. This method presents significant enrichment for clustering analysis that can be applied to different domains. • Integrated static and time-series data for comprehensive retail branch clustering. • Successfully applied the approach to a Spanish telecommunications company data. • Addressed big data variety by proposing hierarchical clustering for data integration. • The proposed approach extends beyond retail and is applicable to diverse domains.

BackgroundHyperlactatemia is common in the intensive care unit (ICU) and relevant to prognosis, while the process of lactate normalization requires a relatively long period. We hypothesized that the dynamic change in base excess (BE) would be associated with ICU mortality and lactate clearance.MethodsWe performed a retrospective cohort study of adult patients with hyperlactatemia admitted to the ICU from 2016 to 2021. The patients were divided into two groups according to whether the peak BE in 12 h was reached in the first 6 h. We compared ICU mortality and lactate clearance at 6 and 12 h after ICU admission.ResultsDuring the study period, 1,608 patients were admitted to the ICU with a lactate concentration of >2.0 mmol/L and stayed in the ICU for >24 h. The mortality rate was 11.2%. The patients were divided into two groups according to whether the peak BE was reached in the first 6 h following ICU admission: Peak BE12h ≤ 6h and Peak BE12h > 6h. The patients were also recorded as whether bicarbonate treatment was received (bicarbonate group, CRRT included) or not (non-bicarbonate group). Furthermore, lactic acid clearance patterns were identified by time-series clustering (TSC) using various algorithms and distance measures. We compared ICU mortality and lactate clearance at 6 and 12 h after ICU admission with logistic regression. After adjustment for other confounding factors, we found that Peak BE12h > 6h was independently associated with ICU mortality with an odds ratio of 2.231 (p = 0.036) in the bicarbonate group and 2.359 (p < 0.005) in the non-bicarbonate group. In addition, based on the definition of >10% lactate clearance at 6 h or >30% at 12 h, we found that Peak BE12h ≤ 6h had 85.2% sensitivity and 38.1% specificity for effective lactate clearance. In time-series clustering analysis, four categories were discriminated, and pattern of lactic acid clearance reveals the early prognostic value of BE in clearance of lactic acid.ConclusionA prolonged time to reaching the peak BE was independently associated with ICU mortality. In patients with hyperlactatemia, Peak BE12h ≤ 6h could be used as an indicator to predict effective lactate clearance.

An autocorrelation incremental fuzzy clustering framework based on dynamic conditional scoring model