diff --git a/mobile/lib/services/machine_learning/face_ml/face_clustering/face_clustering_service.dart b/mobile/lib/services/machine_learning/face_ml/face_clustering/face_clustering_service.dart
index 581f447aca..f8c180b4f7 100644
--- a/mobile/lib/services/machine_learning/face_ml/face_clustering/face_clustering_service.dart
+++ b/mobile/lib/services/machine_learning/face_ml/face_clustering/face_clustering_service.dart
@@ -111,7 +111,7 @@ class FaceClusteringService {
       try {
         switch (function) {
           case ClusterOperation.linearIncrementalClustering:
-            final result = FaceClusteringService._runLinearClustering(args);
+            final result = FaceClusteringService.runLinearClustering(args);
             sendPort.send(result);
             break;
           case ClusterOperation.dbscanClustering:
@@ -185,7 +185,7 @@ class FaceClusteringService {
     _inactivityTimer?.cancel();
   }
 
-  /// Runs the clustering algorithm [_runLinearClustering] on the given [input], in an isolate.
+  /// Runs the clustering algorithm [runLinearClustering] on the given [input], in an isolate.
   ///
   /// Returns the clustering result, which is a list of clusters, where each cluster is a list of indices of the dataset.
   ///
@@ -245,6 +245,65 @@ class FaceClusteringService {
     }
   }
 
+  /// Runs the clustering algorithm [runLinearClustering] on the given [input], in computer, without any dynamic thresholding
+  Future<Map<String, int>?> predictLinearComputer(
+    Map<String, Uint8List> input, {
+    Map<int, int>? fileIDToCreationTime,
+    double distanceThreshold = kRecommendedDistanceThreshold,
+  }) async {
+    if (input.isEmpty) {
+      _logger.warning(
+        "Linear Clustering dataset of embeddings is empty, returning empty list.",
+      );
+      return {};
+    }
+
+    // Clustering inside the isolate
+    _logger.info(
+      "Start Linear clustering on ${input.length} embeddings inside computer isolate",
+    );
+
+    try {
+      final clusteringInput = input
+          .map((key, value) {
+            return MapEntry(
+              key,
+              FaceInfoForClustering(
+                faceID: key,
+                embeddingBytes: value,
+                faceScore: kMinHighQualityFaceScore + 0.01,
+                blurValue: kLapacianDefault,
+              ),
+            );
+          })
+          .values
+          .toSet();
+      final startTime = DateTime.now();
+      final faceIdToCluster = await _computer.compute(
+        runLinearClustering,
+        param: {
+          "input": clusteringInput,
+          "fileIDToCreationTime": fileIDToCreationTime,
+          "distanceThreshold": distanceThreshold,
+          "conservativeDistanceThreshold": distanceThreshold,
+          "useDynamicThreshold": false,
+        },
+        taskName: "createImageEmbedding",
+      ) as Map<String, int>;
+      final endTime = DateTime.now();
+      _logger.info(
+        "Linear Clustering took: ${endTime.difference(startTime).inMilliseconds}ms",
+      );
+      return faceIdToCluster;
+    } catch (e, s) {
+      _logger.severe(e, s);
+      rethrow;
+    }
+  }
+
+  /// Runs the clustering algorithm [runCompleteClustering] on the given [input], in computer.
+  ///
+  /// WARNING: Only use on small datasets, as it is not optimized for large datasets.
   Future<Map<String, int>> predictCompleteComputer(
     Map<String, Uint8List> input, {
     Map<int, int>? fileIDToCreationTime,
@@ -286,6 +345,41 @@ class FaceClusteringService {
     }
   }
 
+  Future<Map<String, int>?> predictWithinClusterComputer(
+    Map<String, Uint8List> input, {
+    Map<int, int>? fileIDToCreationTime,
+    double distanceThreshold = kRecommendedDistanceThreshold,
+  }) async {
+    _logger.info(
+      '`predictWithinClusterComputer` called with ${input.length} faces and distance threshold $distanceThreshold',
+    );
+    try {
+      if (input.length < 100) {
+        final mergeThreshold = distanceThreshold + 0.06;
+        _logger.info(
+          'Running complete clustering on ${input.length} faces with distance threshold $mergeThreshold',
+        );
+        return predictCompleteComputer(
+          input,
+          fileIDToCreationTime: fileIDToCreationTime,
+          mergeThreshold: mergeThreshold,
+        );
+      } else {
+        _logger.info(
+          'Running linear clustering on ${input.length} faces with distance threshold $distanceThreshold',
+        );
+        return predictLinearComputer(
+          input,
+          fileIDToCreationTime: fileIDToCreationTime,
+          distanceThreshold: distanceThreshold,
+        );
+      }
+    } catch (e, s) {
+      _logger.severe(e, s);
+      rethrow;
+    }
+  }
+
   Future<List<List<String>>> predictDbscan(
     Map<String, Uint8List> input, {
     Map<int, int>? fileIDToCreationTime,
@@ -335,7 +429,7 @@ class FaceClusteringService {
     return clusterFaceIDs;
   }
 
-  static Map<String, int> _runLinearClustering(Map args) {
+  static Map<String, int> runLinearClustering(Map args) {
     // final input = args['input'] as Map<String, (int?, Uint8List)>;
     final input = args['input'] as Set<FaceInfoForClustering>;
     final fileIDToCreationTime = args['fileIDToCreationTime'] as Map<int, int>?;
diff --git a/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart b/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
index 68cae30af8..48feb1ea34 100644
--- a/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
+++ b/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
@@ -234,33 +234,11 @@ class ClusterFeedbackService {
         maxClusterID++;
       }
     } else {
-      // final clusteringInput = embeddings
-      //     .map((key, value) {
-      //       return MapEntry(
-      //         key,
-      //         FaceInfoForClustering(
-      //           faceID: key,
-      //           embeddingBytes: value,
-      //           faceScore: kMinHighQualityFaceScore + 0.01,
-      //           blurValue: kLapacianDefault,
-      //         ),
-      //       );
-      //     })
-      //     .values
-      //     .toSet();
-      // final faceIdToCluster =
-      //     await FaceClusteringService.instance.predictLinear(
-      //   clusteringInput,
-      //   fileIDToCreationTime: fileIDToCreationTime,
-      //   distanceThreshold: 0.23,
-      //   useDynamicThreshold: false,
-      // );
       final faceIdToCluster =
-          await FaceClusteringService.instance.predictCompleteComputer(
+          await FaceClusteringService.instance.predictWithinClusterComputer(
         embeddings,
         fileIDToCreationTime: fileIDToCreationTime,
-        distanceThreshold: 0.30,
-        mergeThreshold: 0.30,
+        distanceThreshold: 0.22,
       );
 
       if (faceIdToCluster == null || faceIdToCluster.isEmpty) {